1. What Is Facial Recognition? Facial recognition is a way of using software to determine the similarity between two face images in order to evaluate a claim. The technology is used for a variety of purposes, from signing a user into their phone to searching for a particular person in a database of photos.
2. What Is Facial Characterization? Facial characterization refers to the practice of using software to classify a single face according to its gender, age, emotion, or other characteristics. Facial classification is distinct from facial recognition, whose purpose is instead to compare two different faces. Facial characterization is often confused with facial recognition in popular reporting, but they are actually distinct technologies. Many claims about the dangers of facial recognition are actually talking about characterization.
3. How Does Facial Recognition Work? Facial recognition uses computer-generated filters to transform face images into numerical expressions that can be compared to determine their similarity. These filters are usually generated by using deep “learning,” which uses artificial neural networks to process data.
4. How Accurate Is Facial Recognition? Facial recognition is improving rapidly, but while algorithms can achieve very high performance in controlled settings, many systems have lower performance when deployed in the real world. Summarizing the accuracy of a facial recognition system is difficult, however, as there is no single measure that provides a complete picture of performance.
5. What Are Similarity Scores? Similarity scores provide feedback to human operators about how similar the algorithm believes two images are. These scores can be misunderstood and are often treated as providing more authoritative information than they really do because of something known as the “prosecutor’s fallacy.”
6. What Are Comparison Thresholds? Facial recognition systems face a trade-off between low false negative rates and low false positive rates. Comparison thresholds are a way of using the similarity scores calculated by facial recognition algorithms to tune a system’s sensitivity to these two types of errors. Thresholds are adjusted to account for trade-offs between accuracy and risk when returning results to human adjudicators.
7. Is Facial Recognition Biased? Demographic differences in facial recognition accuracy rates have been well documented, but the evidence suggests that this problem can be addressed if sufficient attention is paid to improving both the training process for algorithms and the quality of captured images.
8. What Does This Mean? Facial recognition is usually discussed only in the context of its most dystopic applications, but it is a multifaceted tool that can be applied to a range of different problems. Facial recognition is used to aid human decisionmaking rather than replace it. Human oversight helps to mitigate the risk of errors. Operators need to understand how system performance can be affected by deployment conditions in order to put in place the right safeguards to manage trade-offs between accuracy and risk. A better understanding of the issues covered in this report will help ensure this technology can be deployed safely in ways that let us capture its benefits while managing risks.
This report was funded in part by the Department of Homeland Security as part of its homeland security mission to defend the homeland while upholding our nation’s values.
This report does not constitute the official position of the Department of Homeland Security. Any questions can be directed to the Office of Public Affairs at the Office of Biometric Identity Management.
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Facial recognition is an advanced technology that helps in discerning and identifying human faces from an image or video. A system employed to perform facial recognition uses biometrics to map facial features from the photo or video. It compares this information with a large database of recorded faces to find a correct match.
Computer scientist Ross Micheals demonstrating the facial recognition setup of his organization (Photo Credit : National Institute of Standards and Technology
Facial recognition is touted to be one of the top 3 methods of biometric recognition to identify people by measuring some aspect of individual physiology or anatomy. Facial recognition is the fastest-growing biometric technology and is expected to grow to $7.7 billion by 2022. This is because facial recognition has a wide range of commercial applications and is relatively simple to set up. It can be used for everything from surveillance to targeted marketing.
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History of Facial Recognition
Facial recognition technology gained popularity in the early 1990s when the United States Department of Defense was seeking a technology that could spot criminals who furtively crossed borders. The Defense Department roped in eminent university scientists and experts in the field of facial recognition for this purpose by providing them with research financing.
Facial recognition made bold headlines in early 2001 immediately after it was first used in a public space—at Super Bowl XXXV in Tampa—by the law enforcement authorities to search for criminals and terrorists among the crowd of thousands of spectators. Soon after that, facial recognition systems were installed in other sensitive parts of the US to keep track of felonious activities.
Although facial recognition is the fastest-growing biometric technology, it also happens to be the most controversial. After the 9/11 tragedy, many people supported the use of this new technology, but as the technology made deeper inroads to our lives, many realized that it could pose a threat to individual privacy and could also potentially lead to identity theft. No matter which side of this debate you’re on, it is worth knowing how this fast-growing technology works and what it can do.
How Facial Recognition Works
A facial recognition setup consists of advanced cameras that capture photos of people who pose or simply walk by, and sophisticated software working on those pictures will attempt to find the right match from the vast database to identify the person(s) in the image. Now, let’s take a closer look at the technical details of how these systems work.
As mentioned earlier, facial recognition methods vary slightly, depending on the application and manufacturer, but they generally involve a series of steps that serve to capture, process, analyze and match the captured face to a database of recorded images. These basic steps are:
When the facial recognition system is attached to a video surveillance system, the recognition software scans the field of view of the camera for what it detects as faces. Upon the detection of each face-like image on a head-shaped form, it sends the face to the system to process it further. The system then estimates the head’s position, orientation, and size. Generally, a face needs to be turned at least 35 degrees toward the camera for the camera to detect it.
Face detection (Photo Credit : Sylenius/Wikimedia Commons)
The image of the captured face is scaled and rotated so that it can be registered and mapped into an appropriate pose and size. This is called normalization. After normalization, the software reads the geometry of the face by determining key factors, include the distance between the eyes, the thickness of the lips, the distance between the chin and the forehead, and many others. Some advanced face recognition systems use hundreds of such factors. The result of this processing leads to the generation of what is called a facial signature.
An illustration of face normalization (Photo credit: Pixabay)
After forming the facial signature, the system converts it into a unique code. This coding facilitates easier computational comparison of the newly acquired facial data to stored databases of previously recorded facial data.
This is the final stage in which newly acquired facial data is compared to the stored data; if it matches with one of the images in the database, the software returns the details of the matched face and notifies the end user.
Applications of Facial Recognition
A lot of organizations and businesses are using facial recognition, albeit for varying purposes. Governments across the globe are using facial recognition systems at airports to monitor people coming and going from their country. The US Department of Homeland Security, for instance, has a system to identify people who have overstayed their visas or may be under criminal investigation.
Apple’s iPhone first made facial recognition a household term. Since then, most mid-range to high-end smartphones come with a face unlock feature to authenticate the phone. Face unlock is a form of facial recognition that ensures that you are actually you when attempting to access your phone. Apple Face ID is arguably the most robust facial recognition feature out there when it comes to smartphones, with the chance of a random face unlocking the iPhone being just one in 1 million.
Face unlock (Image Credit: Flickr)
Most popular social media companies these days use some form of facial recognition. Facebook uses an algorithm called DeepFace to detect faces when you upload a photo to its platform. Upon uploading the photos, it asks if you want to tag people in the uploaded photos. If you allow this, it automatically detects faces and creates a link to their profiles. Facebook claims that its facial recognition system is 98% accurate.
Interestingly, even some religious groups have started to use facial recognition technology to test the belief of their followers! Many churches are using a facial recognition system called Churchix to scan congregations and record the attendance of the individuals who are present. Facial recognition is helping church management keep track of people regarding the regularity of their church visits.
Isn’t it crazy that we can unlock our phones by just staring at it! The first time when fingerprint locks came in place, my mind was blown. But we have clearly gone many steps ahead! Facial recognition technology has grown at a dizzying rate over the last decade, making it tough to keep up with all of the latest developments. By scanning your face, how does your iPhone recognise you? Why does the customs office at the airport check your face when you enter a country? If you are as curious about facial recognition as I am, don’t worry I did all the research on your behalf. Here’s everything you need to know about Facial Recognition.
What is Facial Recognition?
Facial recognition is a software approach that analyses a video frame or a digital photograph that contains the individual’s face to authenticate or identify their identity. Now these systems work in several ways, but the most common method is to match facial features in a photograph to faces in a database. Facial recognition may be used in a variety of ways. Police officers, for example, can use such technology to identify people they stop. Previously, facial recognition software was only available as a computer application. It is now possible to use it on mobile devices and other technology, like robotics. This opens up a slew of new facial recognition applications. It has recently acquired popularity as a marketing and branding tool. Automated image indexing, video surveillance, and human-computer interactions are only some of the few applications.
How does facial recognition work?
Many people are familiar with facial recognition technology because of the FaceID function used to unlock iPhones (however, this is only one of it’s applications). Typically, face recognition does not rely on an extensive database of photos to establish an individual’s identity; instead, it detects and recognises one person as the exclusive owner of the device, denying others access.
Facial recognition compares the faces of people walking past unique cameras to images of people on a watch list, in addition to unlocking phones. The photos on the watch lists may be of anyone, even people who aren’t suspected of committing any crimes, and they could come from anyplace, including our social media accounts. Facial technology systems function in a variety of ways, but they always work in the same way:
Step 1 – Face Detection
Whether alone or amid a crowd, the camera recognises and locates the image of a face. The person in the picture might be looking straight ahead or in profile.
Step 2 – Face Analysis
After that, the image of the face is gathered and assessed. Most face recognition systems focus on 2D rather than 3D pictures, matching a 2D image with public photos or a database. The application analyses the geometry of your face. The distance between your eyes, the depth of your eye sockets, the distance between your forehead and chin, the shape of your cheekbones, and the contour of your lips, ears, and chin are crucial factors to consider. The aim is to recognise the critical facial landmarks for distinguishing your face.
Step 3 – Converting the Image to Data
The face capture technique turns analogue information (a face) into a collection of digital information based on the person’s facial characteristics (data). Your facial analysis has been converted to a mathematical formula. A faceprint is a name given to the numerical code. Each person’s faceprint is unique, just as each person’s thumbprint is unique.
Step 4 – Finding a Match
Your faceprint is then compared to a database of other people’s faces. For example, the FBI has access to up to 650 million images gathered from state databases. Any photo on Facebook that has been tagged with a person’s name becomes part of Facebook’s database, which may be used for facial recognition. If your faceprint matches an image in a facial recognition database, a decision is made.
Facial recognition is the most natural of all biometric measures. This makes intuitive sense, given that we often recognise ourselves and others by looking at faces rather than thumbprints and irises. It is believed that face recognition technology regularly affects more than half of the world’s population.
Why Facial Recognition is so popular in today’s time
Face recognition can aid in detecting terrorists and other criminals on a government level. On a more personal level, face recognition might be used as a security measure to protect personal electronics and surveillance cameras.
Even the availability of face recognition technology can act as a deterrent, especially in cases of minor wrongdoing. Apart from physical security, cybersecurity offers its own set of benefits. Businesses may employ face recognition technology to replace passwords when accessing computers. In theory, the approach cannot be hacked because there is nothing to steal or update, unlike a password.
When the technology becomes more widely available, customers will pay at stores using their faces rather than credit cards or cash. This might cut down on time spent in checkout lines. Face recognition provides a rapid, automated, and seamless verification experience. It does not require contact, as it does with fingerprinting or other security measures – which is especially important in the post-COVID environment.
The recognition of a face takes less than a second, which gives firms that use facial recognition an advantage. Businesses demand secure and speedy answers in an era of cyber-attacks and solid hacking tools.
Removing Bias from Stop and Search
The public’s discontent with illegal stops and searches is a source of controversy for cops; facial recognition technology might help improve the process. Face recognition technology might help minimize bias and reduce stops and searches of law-abiding citizens by detecting suspects in crowds using an automated rather than a human method.
Face recognition has a bright future ahead of it. Even if sustaining consistent levels of accuracy is still a challenge, we are still light years ahead of where we were 5-10 years ago. This industry will continue to grow, setting the way for significant financial gains in the coming years. Surveillance and security are two major areas where this technology will considerably impact.
- Disaster Victim Identification (DVI)
- Facial Recognition
- Forensic Symposium
The INTERPOL Face Recognition System (IFRS) contains facial images received from more than 179 countries which makes it a unique global criminal database.
Coupled with an automated biometric software application, this system is capable of identifying or verifying a person by comparing and analysing patterns, shapes and proportions of their facial features and contours.
Factors in facial identification
Unlike fingerprints and DNA, which do not change during a person’s life, facial recognition has to take into account different factors, such as:
- Plastic surgery
- Effects of drug abuse or smoking
- Pose of the subject
Working with good quality images is also crucial. Low or medium quality images may be not searchable in the IFRS system and, if they are, the accuracy of the search and the results themselves can be significantly affected.
An ICAO standard passport photo would be ideal, since this is a frontal image of the subject that has even lighting on the face and a neutral background.
How does it work?
When a facial image (probe image) is entered into the system it is automatically encoded by an algorithm and compared to the profiles already stored in the system. This results in a ‘candidate’ list of the most likely matches.
We always carry out a manual process – we call this Face Identification – to verify the results of the automated system. Qualified and experienced INTERPOL officers examine the images carefully to find unique characteristics that can lead to one of the following results: ‘Potential candidate’, ‘No candidate’ or ‘Inconclusive’.
This information is then passed on to the countries that provided the images, and to those that would be concerned by the profile or a match. All information is handled in line with INTERPOL’s Rules on the Processing of Data.
Cross-checking with INTERPOL Notices
All face images in Notices and Diffusions requested by member countries are searched and stored in the face recognition system, provided they meet the strict quality criteria needed for recognition.
Member countries can also request a ‘search only’ in the face system, for example, to carry out a check on a person of interest at airports or other border crossings. The results are returned quickly to enable immediate follow-up action, and images are not recorded in the system.
Bringing experts together
As this computerized biometric comparison technology is still in its infancy in most countries, standards and best practices are still in the process of being created, and INTERPOL is contributing to this.
Held every two years, INTERPOL’s International Fingerprint and Face Symposium provides an opportunity for experts from around the world to share best practice and latest developments.
We also host meetings of the Face Expert Working Group twice a year. This is INTERPOL’s advisory group for new technology, identification procedures, training needs and for producing official documents to assist member countries in this field.
The Expert Group produced a best practice guide for the quality, format and transmission of facial images to promote accurate and effective recognition. We strongly encourage our member countries to use the facial recognition service and to follow the recommendations.
Developing best practices
While facial recognition systems have huge potential for national safety and security, they require a robust governing structure in order to protect human rights and personal data.
INTERPOL, along with the World Economic Forum (WEF), the United Nations Interregional Crime and Justice Research Institute (UNICRI) and Netherlands Police, co-designed a policy framework to promote the responsible and transparent use of facial recognition technology in law enforcement investigations.
The result of a global, multi-stakeholder consultation, this white paper was published in October 2021. INTERPOL will raise awareness of the initiative via its global membership and the framework will be tested by law enforcement agencies in the first quarter of 2022.
Facial recognition systems are now available as part of video recording software systems.
The Technology for Facial Recognition
There are two primary methods for capturing and matching facial information.
The first measures various features of a person’s face, such as the distance between the eyes, or the position of the mouth to the nose. These geometric measurements or vectors are then stored in a database for later comparison.
The second method is far more complex. It captures the full facial image and uses as much information as it can. It then uses various computer algorithms, including machine learning, to build a set of definition data. This statistical database increases the reliability, as well as the cost of the system.
There are some other methodologies used such as 3-D modeling, skin texture analysis, and the use of thermal cameras. Some of these technologies require more computer resources, so there is a trade-off of performance versus cost. Facial recognition systems used to be standalone systems, but today the analytic software is available as part of IP camera recording systems.
The Camera Environment is Important
No matter what technique is used, facial recognition works better when there is a good set of facial images to work with. It is very important to have consistent and controlled lighting, camera resolution, face position, and limited motion.
The lighting on the face must be bright enough so that the camera sensor doesn’t introduce noise. There also needs to be enough light to provide enough contrast for the recognition algorithm. Many of these systems require at least 300 to 500 lux of illumination. This is about the light we see in a normal office working environment.
The lighting should be consistent so that shadows don’t introduce spurious or false representation of the face.
IP Camera resolution depends on the type of recognition system used and the total field of view. In general, the wider the field of view the more resolution you will need. Many systems require a certain minimum number of pixels across certain facial features. For example, you may require 60 pixels between two eyes (interpupillary distance). Some other systems require at least 80 to 120 pixels across the face. Once we know the resolution requirements of the facial recognition system, we can calculate the resolution of the camera.
Here’s an example. If we use a system that uses the distance between two eyes, we first need to know what this distance is expected to be. A database and study were done for the 1988 Anthropometric Survey of US Army Personnel . In this study, the mean dimension for men was about 65 mm, and women averaged about 62 mm. The largest distance measured was 74 mm, while the shortest distance was about 55 mm. To calculate the IP camera resolution required for this type of facial recognition, it is best to use the shortest dimension of 55 mm.
- We need 60 pixels per 55 mm or 60/55 = 1.09 pixels/mm.
- Next, we need to decide what field of view (FOV) we would like. Suppose we decide that the horizontal field of view is 1524 mm (which is about 5 ft wide).
- To achieve 1.09 pixels/mm across the 1524 mm FOV, we need 1.09 x 1524 which equals 1663 horizontal pixels.
- Next, we look for a camera that has at least this number of horizontal pixels. More is better.
- A 2 Megapixel (1920 x 1080) camera exceeds this requirement, while a 1-megapixel camera (1280 x 1024) would not work.
The 2 Megapixel IP camera, with 1920 pixels across the horizontal is best for this application. As an example of IP cameras available, take a look at the Hanwha (Samsung) IP cameras available.
If we want to view a larger area, then we would need to increase the camera resolution. For example, a 10 ft wide area, would require twice the resolution. Take a look at our article, Calculating What You Can See With Your IP Camera, for more information.
One facial recognition software product has been used in churches to determine who has attended. Churchix software works quite well in the church where the lighting level is right, everyone is facing forward, and they even have the same expression on their face (most of the time). In many applications, the challenge is to assure that everyone you are scanning is looking in the right direction.
It is best to install a face recognition system in a doorway or gate area where it is likely that everyone is facing the right direction, and they are approaching the camera a few people at a time. There are some systems that can be used in less constricted environments. As an example, take a look at our article, How Face Recognition Works in a Crowd .
Camera systems need to be able to capture the face, and if there is too much motion, it could produce a bad video image. To improve performance when people are moving quickly, facial systems require cameras that can support high frame rates. Usually, 30 fps is adequate, but if more motion is expected, a camera with 60 fps may be required. Facial recognition algorithms need some time to process data, so if there are too many people flowing through the system, the recognition process may not be fast enough. Higher performance computer servers are required as the number of detections increase.
There are two major applications for facial recognition systems. The first can detect predetermined people in a crowd, while the second is used for door entry control. These systems are available as part of video management software or as standalone door control units.
Detection of Certain People in a Crowd
Facial recognition systems are used to recognize good customers in a store or even a gambling casino. They are also used to detect criminals. Recognizing a face in a crowd is harder than biometric face recognition used for door access control. The latest 3D facial recognition systems are getting much better at finding people in a crowd. The systems are most effective at choke points where a few people at a time can pass across the camera.
Biometric Facial Door Access Control
Facial recognition systems are also used for door access control. Simple systems require the person to stand in front of the door access unit. This method controls the light, position, and field of view so that these simple facial door control systems can operate correctly.
Please contact us for assistance in selecting the right facial recognition system for your application. We can be reached at 800-431-1658 in the USA, and at 914-944-3425 everywhere else, or use our contact form.
What Kintronics Does
Kintronics provides everything you need to create a complete surveillance and security system. We are an engineering and consultation company that sells complete IP security solutions at the very best prices.
USA Phone: 1-800-431-1658
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Remember the way iPhone X ads caught your eye – Your face as your password to protecting the data and information on your phones was ultimate. They not only created a buzz with face recognition but also literally handed over the might of face recognition or facial recognition technology to millions of phone users to benefit and enjoy.
What exactly is Facial Recognition?
The ability to map facial features mathematically and store data as face prints to uniquely identify and verify a face based on facial contours is Facial Recognition. It’s a biometric-based software application technology that functions based on nodal points of a human face. The values associated with these nodal points help to identify or verify each unique face. One can accurately and quickly find faces against captured data; however, technology is evolving with new 3D modeling approaches to better assist in overcoming current constraints
The Facial Recognition Advantage
Facial recognition is a non-contact technique not requiring any interaction with the user/person. This adds an edge for surveillance, security, and monitoring to map time and presence that can be done from a distance at a comparatively low cost.
One needs to remember that the technology is light-sensitive and less favorable in low light and less effective when facial expressions vary. To understand these constraints, one needs to comprehend how facial recognition works.
How does it work?
Every person’s face is unique like a fingerprint, and the technology mapping this uniqueness behind facial recognition is complex, but the process is simple. It’s akin to your process of identifying a face – you get familiar with the facial features like eyes, nose, mouth and they come together to help you recognize. It’s the same here but on a grand algorithmic scale.
Your picture is captured from a photo/video or live; the facial recognition software reads the geometry of your face – like the distance between your eyes, forehead to chin and identifies facial landmarks (software can identify 68 or more landmarks) and facial signatures are created.
The facial signature now a mathematical formula is then compared to another facial image for search and finally, a determination is made.
This basic facial recognition process looks for faces to identify like the Instagram filters – they seek features like eyes, nose, mouth and then use algorithms to lock a face and determine the direction it’s looking at.
The Face ID on your iPhone measures the distance between your facial features and every time you look at your phone to unlock, the camera looks at you – measures and confirms your identity.
The process to identify a stranger is similar but on a larger scale – algorithms of that face are used to compare and confirm across a huge database that runs into millions. Thus, facial identification in this situation is difficult, and hence different technologies are used to identify a face.
Omnipresence of 2D Imaging Technology
The most popular technology that facial recognition relies on is 2D images for its sheer convenience. Mainly, a whopping majority of photos taken and available in databases and open-source intelligence (like Facebook, etc) are 2D images.
However, 2D images have 2 strong constraints.
1. They are not precise as these flat images lack identifying features like depth – the length of your nose, etc.
2. They are not clearly visible in dark or shadowy lighting conditions.
3D images with IR Camera for depth and clarity
This is achieved with a technique called lidar – The 3D camera blasts a harmless IR matrix – simply puts a wall of laser; that reflects from your face. The IR camera picks this reflection and measures how long it takes for each bit of light to return. Obviously, the ray from your nose returns earlier to say your eyes or ear. It then creates a unique depth map of your face. This 3D image when used alongside the 2D photos significantly increases the accuracy of facial recognition software.
Thus, Lidar imaging is this 3D indentation your face has left in the IR Mesh; imagine – like a mask of laser – where your nose is deeper than your eyes.
Better Facial Recognition with Thermal Imaging
The second constraint of low light, low visibility is well addressed by using a thermal imaging camera. Thermal imaging cameras rely on IR Light that the objects emit. Warm objects emit IR Light, more advanced and expensive technology is available to detect subtle temperature differences too.
Some of the simple ways used to identify a face with a thermal image
Use of multiple photos
A thermal imaging camera captures more than one photo of a subject. Each time it focuses on a different spectrum of IR light like long, medium, or short waves to better gauge details.
Face print with blood vessels map
The blood vessel map is unique and can help formulate facial fingerprints. One distance finds out that distance between organs or identifies scars and bruises too.
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Innovations Explained: How Does Facial Recognition Work?
Facial recognition software has come a long way in recent years. It used to only be in sci-fi, like Star Trek or Minority Report… but now it’s part of our everyday lives!
Perhaps you first discovered facial recognition from logging into your phone – or maybe it was Facebook tagging images for you.
In any case, your first experience with facial recognition surely won’t be your last. This new technology is becoming more sophisticated, and it’s all over the place these days – from airports and shops to social media sites, phones and more.
Today we’ll explore this interesting topic – including how facial recognition works, a bit about the history, and of course… tips on how to turn it off (when possible)!
What is Facial Recognition?
Facial recognition is any program that uses AI to identify people using images of their face.
The first facial recognition software could only analyze clear, clean images; however, these days it can even detect faces in motion, such as a video.
The technology was originally developed in the mid-1960s. It was funded by U.S. intelligence agencies and the military, who used it for… well, lots of things (and they still use it today).
How does Facial Recognition work?
While each facial recognition software works slightly different from the next, there are generally more similarities than differences. The process most technology uses is called Facial Scanning.
Facial scanning measures up to 80 distinct features on your face. This includes everything from the distance between your eyes, the width of your eyes, the size of your nose, the distance from your chin to your forehead, and so on.
Image source: G2 Learning Hub
These are all called “nodal points”. They’re essentially dots at important landmarks on your face – and together they create a “facial map”.
Similar to your fingerprint, everyone has a unique facial map. In a way, facial recognition works in a similar way to a fingerprint scanner — but it’s 3D instead of 2D.
How is Facial Recognition used?
In most cases, your phone (or another device with facial recognition technology) will ask to perform a scan of your face. Some facial recognition programs just need a single image, but most require a few shots at different angles.
Your device uses these images to create that facial map, which it stores in a secure location. Then, every time you want to access something using facial recognition, it refers to that facial map.
As you can probably guess, the software is not perfect. It keeps getting better, but we still hear cases of people using photos to unlock people’s phones. This is because the facial maps are limited to a certain amount of nodal points. If two people have very similar facial features, it’s possible they could be mistaken for one another by a computer.
So, while facial recognition is quite powerful, it’s still not perfect. That means you may want to turn it off in some cases. But how?
How to turn off facial recognition
Fortunately, it is possible – but it depends on the platform. Any time you sign up for facial recognition, your facial map gets stored. Therefore, you’ll need to visit each site or device you’ve created a facial map at.
For most people, this is Facebook and/or their phone. Let’s walk through each:
How to turn off Facial Recognition on Facebook
Facebook doesn’t make it easy. If you want to turn off facial recognition, follow these steps:
1. Open the Facebook mobile app
2. Bring up your profile
3. Choose “More,” then select “View Privacy Shortcuts.”
4. Select “More Settings,”
5. Choose “Face Recognition,” and turn it off.
How to turn off Facial Recognition on your phone
1. On your device, go to “Settings”
2. Open “Face ID and Passcode”
3. Choose “Toggle Off.”
1. On your device, go to “Settings”
2. Go to “Lock Screen and Security”
3. Find the facial recognition option and turn it off.
So now you know all about how facial recognition works – plus how to turn it off. Thanks for reading, and we’ll see you again soon!
One comment on “ Innovations Explained: How Does Facial Recognition Work? ”
Well above my ability as a elderly pensioner but interesting. Thank you for the update but still to complicated for me.
Cheers and happy new year to all.
Date: October 23, 2020
Author: Kajal Mishra
Facial recognition is a biometric identification process to identify, verify, and authenticate the person using facial features from any photo or video. Facial recognition system works on comparing facial biometric patterns of the face of interest with the database of known faces to find the match.
Advancements in security & surveillance have changed the way data is captured and how to drive actions and make the best use of data in the future. Security systems can be as fundamental as the video camera to as complex as the biometric system to monitor, detect and record the intrusion. Today’s surveillance market has evolved and moved beyond these traditional cameras, and technologies like biometric facial recognition is taking centre-stage. The use of Machine Learning and Artificial Intelligence technologies empowers facial recognition to be the most effective contactless biometric system.
How Does Facial Recognition Technology Work?
Recognizing faces may seem a natural and easy-going process but creating the facial recognition technology from scratch is challenging. It is quite difficult to develop an algorithm which works well with varying conditions like large datasets, low illumination, pose variations, occlusion, varying poses, etc. Despite challenges during technology implementation, facial recognition technology is continuously increasing due to its non-invasive and contactless features.
So how does facial recognition system work? Technologies may vary, but here are the basic steps:
Step 1: Face Detection
The facial recognition process starts with the human face and the necessary facial features pattern of the person to be identified. When we think of a human face, we probably also think of the very basic set of features, which are eyes, nose, and mouth. Similarly, facial recognition technology also needs to learn what a face is and how it looks. This is done by using deep neural network & machine learning algorithms on a large database of images with human faces looking at different angles or positions.
The process starts with human eyes, which is one of the most accessible features to detect, and then it proceeds to detect eyebrows, nose, mouth, etc. by calculating the width of the nose, distance between the eyes, and the shape & size of mouth. Once it finds the facial region, multiple algorithm training is performed on large datasets to improve the algorithm’s accuracy to detect the faces and their positions.
Step 2: Feature Extraction
Once the face is detected, the software is trained with the help of computer vision algorithms to detect the facial landmarks (eyebrow corners, eyes gap, tip of the nose, mouth corners, etc.) Each landmark is considered as nodal points, and each face has approximately 80 nodal points. These landmarks are the key to distinguish each face present in the database.
After this, the registered face in the database is adjusted in position, size and scale to match with user’s face. It would help whenever the user’s face moves or expression changes; the software will accurately recognize it.
Step 3: Face Representation
When the facial feature is extracted, and landmarks, face position, orientation & all key elements are fed into the software, the software generates a unique feature vector for each face in the numeric form. These numeric codes are also called Faceprint, similar to Fingerprint in contact biometric system. Each code uniquely identifies the person among all the others in the training dataset. The feature vector is then used to search through the entire database of enrolled users during the face detection process.
Step 4: Face Matching
After generating the unique vector code, it is compared against the faces in the database. The database has all the information of registered users. If the software identifies the match for exact feature in the database, it provides all the person’s details. If the compared featured vector value is below a certain threshold value, the feature-based classifier returns the id of the match found in the database.
There are some challenges while performing the face matching process. If the image to be matched is in 3D format and the database image is also 3D, then matching will occur without any changes being made in image. If the database image is in 2D format and the image to be matched is 3D, then the matching process would take some 3D image changes. In 3D image, the facial expression and feature pattern will be different from the database image. So, once the facial landmarks are measured, a step-by-step algorithm is applied to the 3D image to get it converted into 2D image, which becomes ideal to be a potential match.
The process to compare one face to another face in the database or one-to-one mapping (1:1) is called Face Verification. But, if we compare one face to all the faces/ images from the database (1: N) to find the potential match, it’s called Face Identification.
In the COVID-19 outbreak, contact tracing through biometric identification has become the widely adopted tool to reduce the virus spread. From monitoring temperatures to identifying people without mask, various countries are including facial recognition into their systems and replacing it with contact biometric systems. It works on a large algorithmic scale, and the software stores or having access to abundance of data. As per the study, with nearly half of all American adults having their images are stored in one or more facial recognition databases used by various government agencies for public protection.
Use of artificial intelligence and machine learning technologies has made the facial recognition process carried out in real-time. The algorithm captures incoming 2D & 3D images depending upon device’s characteristics and analyses it using algorithmic scale without any error by matching it with the database image. The integration of smart technologies with high computing techniques makes the facial biometric system one of the safest and reliable online identity verification solutions.
Have you ever wondered why your face is scanned at customs when you enter a country? Or how the new iPhone can unlock using your face instead of a password? The answer is facial recognition/authentication technology.
What is Facial Recognition?
Facial Recognition System, known as a biometric software application, is competent in distinctively recognizing or verifying an individual by comparing and examining patterns on the basis of an individual’s face-based contours. Simply put, this software can analyze your features, match them with information in a database and identify who you are.
How does it work?
a) Face Detection:
b) Face Analysis:
c) Converting an Image to Data:
d) Finding a Match:
Your code is then contrasted against a databank of other faceprints. This databank has images with detection that can be compared. The technology then recognizes a match for your precise features in the given database. It returns with the corresponding match and attached information such as the name and address.
Sunartek’s Facial Recognition Systems
Facial Recognition systems at Sunartek, not only capitalize on a person’s facial dimensions but are also alert when the faces don’t match. Considering one’s picture as a storage for a centralized server, our Facial Recognition Systems can identify the same picture encountered across diverse cameras. With the use of biometric software and pattern analyzing skills, our Facial Recognition Systems intends to heighten security parameters for public safety and different businesses/services. We have designed and developed the fastest and accurate Facial Recognition Systems that operate cost-effectively on IP cameras, smartphones and body-worn devices, addressing two major concerns: Security and Customer Data Enrichment. Facial Recognition Systems from Sunartek ensure utmost security across the banking sector, corporate arena, governmental organizations and defense industry. In fact, when considering the banking sector, despite several security measures taken by financial institutions worldwide, banking fraud is still a recurring problem. While Banks have become sophisticated at using one-time passwords to authorize transfers or access accounts, there’s still room for improvement. One possible solution is biometric identification via facial recognition technology. Imagine! Instead of those OTPs (one-time passwords), what if you could manage transactions by looking at your PC or smartphone? Biometric online banking is another beneficial trait of face recognition, right there. This is where Sunartek’s Face Authentication for Remote Banking Services (FARBS System) can prove to be beneficial. Sunartek’s FARBS system is an ‘API-based solution’ that can be integrated with a banking application either on a mobile phone or a mobile banking web server. The API is available for Android, iOS, Windows, and Linux platforms.
Beneficial features of the FARBS System:
• Hackers will not have access to the customer’s account, even if they bypass mobile security.
• More secure than conventional mobile phone-based facial recognition as profiles are stored on the bank’s server.
• Platform Independent: The API-based solution makes it easy to integrate
• Works in all conditions: Low lighting, day/night contrasts & more
• Works effectively even when the user is wearing any cap, spectacles, etc.
• Verification time: Less than 3-seconds
• Dual-layer Liveness Detection: Ability to detect the first layer i.e. front-end from a mobile application (platform-dependent) & the second layer i.e. back-end from server verification.
• Works with normal internet bandwidths for any requirement regarding online banking transactions.
Overall, if used correctly and proportionately, facial recognition can help safeguard the public and improve national security on several fronts. It can do a lot more to increase security in the future – from street crime to airport security, all the way through to assisting those battling addiction, technology has the potential to take operations & security to newer heights.
By RapidAPI Staff // September 14, 2020
Table of Contents
Facial recognition technology uses advanced software, often operated by artificial intelligence (AI) in its increasingly sophisticated methods, to identify faces of people using cameras.
The technology has come a long way in recent years and all indications point to the continued development of this technology until it becomes ubiquitous and foolproof.
How Does Facial Recognition Work?
The starting point for facial recognition is a database of faces for a computer system to comb through to compare an image against its records.
These are usually in the forms of headshots like the ones commonly taken for passports, driver’s licenses, or other identification purposes.
Governments typically have access to large caches of these pictures to establish their database.
Increasingly, private companies, especially social media giants like Facebook, have the capability to keep and store large numbers of images for use in facial recognition programs.
The computer system then measures the proportional angles of the subject’s face, which are unique to each person, and then uses this mathematical formula to locate a match in the database.
This method is increasingly accurate and quick, allowing real-time review of identities.
Concerns about Facial Recognition
Many privacy and personal liberty advocacy groups like the American Civil Liberties Union have expressed their concern about the use of facial recognition on a large scale and all of its potential abuses.
While most people agree that facial recognition could be a valuable tool for law enforcement in preventing crime and terrorism, the real worry is that at some point the technology will be used to track the whereabouts of innocent, law-abiding citizens.
Uses for Facial Recognition Technology
So far, facial recognition is a relatively new technology that is mostly used by law enforcement.
Examples of the uses of facial recognition include finding missing people (especially children), preventing human trafficking, thwarting terrorism, and aiding criminal investigations.
However, there are many applications being developed outside of law enforcement. In healthcare, for example, facial recognition is already being used to monitor a patient’s use of medication, to identify genetic diseases before a provider can notice their development, and more.
The marketing uses for facial recognition are nearly endless. Many retailers have already added facial recognition technology to their stores which they use to track and record customer behavior.
With this information, they can design more effective marketing strategies and better floor layout for their stores.
These are just a few of the exciting (and sometimes terrifying) prospects for the future of facial recognition.
We get a lot of feedback and questions when customers are looking to choose an identity verification provider. These are things like how does Artificial Intelligence (AI) work with Identity verification? How does facial recognition work and is there a human overseeing the process?
Let’s Start With The Face
Identity verification providers look at a wide array of data to make a claim for identity. But, the most important point is to start with the face. It is unique and genuine to all humans, allowing strong conclusions to be made.
For an image analysis, the computer starts by locating a human face in the field and returns all sorts of “face-related” data. Once a face is detected, the next level of processing is defining the “face landmarks.” Which is exactly what it sounds like. The computer analyzes 27 points like the pupil, tip of the nose and eyebrows (Ex.1).
After the landmarks are found, the really cool work begins. Just a few of the features that can be analyzed are:
- Emotion — A list of emotions with their detection confidence for the given face. Confidence scores are normalized, and the scores across all emotions add up to one.
- Gender – The estimated gender of the given face. Possible values are male, female, and genderless.
- Makeup — Whether the face has makeup. This attribute returns a Boolean value for eyeMakeup and lipMakeup.
- Hair — Though some do not have any, the machine can give levels for baldness and what colors are detected.
- Age — The estimated age of the person in years.
The Rest Is Statistics
Once all the analysis is done and the data is presented, comparing these data-points are a quick job for the automation systems. Comparing each point with a percentage that falls within a studied and programmed, standard deviation helps assert claims.
Just like cutting-edge medicine, this is not an exact science. Facial recognition software is improving and sometimes even needs the human eye to comb over the results.
Human and Machine Join Forces
Artificial intelligence (AI) today is only as good as the humans that are teaching it. Companies today like Google and Amazon have Mechanical Turk programs, which crowdsource humans to process image analysis and tell the machine what that image is.
The thought here is that the more image results a machine has in its’ database, the less chance of something new being seen and a result not coming back instantaneously. Identity documents are the same thing.
A hybrid approach allows human expertise and machine learning to really deliver. Our team of developers are trained to constantly monitor and detect new documents that are our AI may not have seen before. They can quickly classify new documents and images to make our learning models even better. By using the best human experts to train the automation, we are ensuring a constant level of improvement.
Until machines can think on their own, a hybrid with human-machine approach is the best way to continually improve. Experts human that drive industry leading AI, will offer the highest level of surety and results.
Learn how facial recognition works with Apple devices
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Face ID is a facial recognition system that replaces Apple’s Touch ID fingerprint scanner on some devices. It uses sensors arrayed around the iPhone’s user-facing camera to scan your face and, if the scan matches the data on file, perform actions like unlocking the phone or authorizing an Apple Pay transaction.
What Is Face ID Used for on iPhone?
Face ID is used for many of the same things as Touch ID. Most important among these are:
- Unlocking the iPhone in place of entering a passcode.
- Supplying your Apple ID password or other passcodes.
- Authorizing Apple Pay transactions.
What Devices Support Face ID?
Face ID is supported by the full iPhone X series: the iPhone X, iPhone XS and XS Max, and iPhone XR.
It’s a safe bet that, just like Touch ID started on the iPhone and has been added to other devices like the iPad, Face ID will eventually appear on other Apple devices such as the iPad.
How Does Face ID Work?
The notch at the top of the iPhone screen on compatible devices is where the sensors used by Face ID are located. These sensors include:
- Dot Projector: This projects over 30,000 invisible dots onto your face. The dots are used to map the structure and depth of your face. That builds a three-dimensional “unique facial map,” according to Apple.
- Infrared Camera: This camera reads the dots from the Projector and captures an image.
- Flood Illuminator: This additional infrared light helps ensure that the system works even in the dark.
The facial map captured by the infrared camera is matched against the data stored on your iPhone to unlock or authorize the Apple Pay transaction. It’s also used to create animoji, animated emojis mapped to your face.
The system is smart and sensitive enough, according to Apple, that it can recognize you even if you change your haircut, wear glasses, grow or shave a beard, age, or wear a cloth mask. That said, there have been some cases in which Face ID has misidentified people, including twins and children who look a lot like their parents.
Is My Face Scan Stored in the Cloud?
No, Face ID face scans are not stored in the cloud. All face scans are stored directly on your iPhone. They are held in the “Secure Enclave,” one of the iPhone’s chips that’s dedicated specifically to securing sensitive data. This is also where fingerprint information created by Touch ID is stored.
How Secure Is My Face Scan?
The way that the Secure Enclave works makes Face ID very secure. Your facial scan itself isn’t actually stored on your iPhone. Instead, when the facial scan is created, it’s converted to a number that represents the scan. That’s stored on your iPhone.
Even if a hacker were able to access the data in your iPhone’s Secure Enclave (which is very unlikely), all they would get is a number, not an actual scan of your face. That means they would not be able to use the data to submit your information to another facial recognition system.
How to Set up and Use Face ID
Setting up Face ID is extremely simple—in fact, you probably did it while setting up your new iPhone. Just position your face in the onscreen frame and roll your head around until the circle around the edge of the frame is filled in. Repeat as many times as the phone tells you and you’re done.
If you skipped that step during setup, you can always go back and add Face ID later. To do this, go to Settings > Face ID & Passcode and follow the onscreen instructions.
You can also add other faces to Face ID on your iPhone including someone you trust as well as alternative pictures of you (with and without a beard or glasses, for example).
How to Disable Face ID
If you need to quickly disable Face ID, press the iPhone’s side button and volume down buttons at the same time. In order to enable Face ID again, you’ll need to re-enter your passcode.
How Does Face ID Compare to Other Smartphone Facial Recognition Systems?
Despite a healthy dose of skepticism when it was announced, Face ID has been a hit with users and critics. The overall consensus is that it’s very accurate, very fast, and very simple. Overall, the transition away from Touch ID to Face ID has been faster and smoother than most people expected.
It’s also proved, once again, how good Apple is at this sort of innovation. When Face ID launched there was one major phone out with this kind of technology: Samsung S8. Unfortunately, that system has been shown to be very easy to fool, including by holding up a photograph. Because of this, the Samsung system appears to not be terribly secure. Samsung won’t allow its facial scans to approve financial transactions (the way Touch ID can on an iPhone).
Use Face ID When Wearing a Face Mask
The iOS 14.5 update allows users to unlock their iPhone X and later using Face ID while they’re wearing a face mask. To use this feature, you’ll need an unlocked Apple Watch Series 3 or later and your iPhone nearby. Just glance at your iPhone, and then your Watch will issue haptic feedback letting you know the iPhone is unlocked.
In iOS 15.4, Apple added mask functionality without a synced Apple Watch. After you re-scan your face, the iPhone will have enough information to unlock based on the visible part of your face.
No, it does not. Face ID is attention-aware and can recognize if your eyes are open and your attention is directed towards the device. This helps to prevent someone from unlocking your device without your knowledge.
Face ID is designed to work with head coverings and eyewear. It should be able to recognize your face even if you are wearing a hat, scarf, glasses, contact lenses, and even many types of sunglasses.
Facial Recognition in a Crowd
There are different methods used for matching facial biometrics. The simplest method measures various features of a person’s face, such as the distance between the eyes, or the position of the mouth to the nose. These geometric measurements or vectors are then coded and stored in a database for later comparison. This type of system is usually used in biometric door access control readers.
The second method is more complex. It uses IP cameras to capture the full facial image and uses as much information as it can. The software then uses various computer algorithms, including machine learning, to build a set of definition data. This statistical database increases the reliability of the facial recognition system. The more complicated face recognition algorithm is used to identify a person in a crowd.
Facial recognition used to identify a person’s face in a crowd is different than biometric access control systems. They have different requirements and challenges.
Door control systems capture the face in a controlled environment. There is a relatively small database of faces to compare. The subsequent comparison is also made in a controlled environment. The lighting conditions and face position are the same for both capture and comparison.
Recognizing a person in a crowd is much more complicated. The faces in a crowd can be oriented differently than the initial picture. The illumination can be different, there could be different facial expressions, and shadows that modify the images detected. The face could be partially obscured by other people of the environment they are in.
How 3D Facial Recognition Works
3D facial recognition extends the traditional methods of facial recognition to live-stream accurate capture and identification. In this system, the three-dimensional geometry of the human face is used.
There are several techniques for capturing the 3D faces. One method uses multiple sensors to create a 3D model of the face. Another method captures the 2D face and then converts it to a 3D image. This transformational process reduces the cost of capture yet maintains reliability.
A standard high-resolution IP camera can be used for capture. The full frame-rate search process has increased levels of difficulty when compared to a controlled environment. The high-performance algorithm used for 3D identification must work even when there is a cluttered background. Subjects are not only in motion, but the direction they are moving is not optimal for face matching purposes. A sophisticated 3D facial recognition system can identify people even when there is motion blurring. It does this by tracking faces through time. The system operates even when there are busy backgrounds, variations in lighting and the possibility of faces being hidden (occluded) by objects or other people.
In the real world, there are multiple people present and each one requires simultaneous detection, tracking, and matching. Faces must be recognized even when there is degradation of facial details because of lossy compression that is present in almost all video transmission and storage.
The better 3D-facial recognition systems are constantly learning how to perform better by utilizing convolutional neural networks which allow them to train the algorithms to deliver ever-increasing performance.
IP Camera System Integration
An IP camera can be used to capture faces in a crowd. The IP camera must be positioned to capture the faces correctly and to provide a minimum of 30 to 35 pixels across the face. To maximize performance the lighting should be well controlled. Cameras that include good wide dynamic range (WDR) are best when there is a large variation of lighting. When it’s dark, the cameras should have enough low-light sensitivity to operate without introducing a lot of noise.
Some video management software (VMS have options for integrated facial recognition. For example, Ocularis from OnSSI integrates a high-performance 3D facial recognition system. This allows the security person to be notified when a person of interest is detected. It also allows the recorded video to be searched for all the instances when a certain person has been recorded. The biometric integration with Ocularis utilizes the C2P software. This combination allows the user to see the recorded video along with the information about the identified individual.
Summary of Facial Recognition
Facial recognition is a challenging biometric. It is especially difficult when recognizing a face in a crowd. 3D facial recognition systems are one of the most reliable ways to provide facial recognition in a crowd. An IP camera and new software algorithms have become available that reliably recognize faces in a crowd.
If you would like help selecting the right facial recognition system for your application, please contact us at 800-431-1658 in the USA, and at 914-944-3425 everywhere else, or use our contact form .
What Kintronics Does
Kintronics provides everything you need to create a complete surveillance and security system. We are an engineering and consultation company that sells complete IP security solutions at the very best prices.
USA Phone: 1-800-431-1658
Fax Number: 914-944-0717
5 Aug 2020 5 August 2020
Every time you open your phone using your face, you’re using a piece of technology called facial recognition.
Facial recognition allows your face to be identified by your phone so you can unlock it and access all of its features.
You’re also using facial recognition when you’re using filters on social media.
It’s not just used by phone companies, the technology is also used in more serious ways – by the police in an attempt to identify criminals, for example.
However, this move has been controversial because the technology hasn’t been completely successful in identifying people from non-white backgrounds.
Here is everything you need to know about facial recognition.
Check out some more stories on the Newsround website:
Facial recognition is technology that makes it possible for a computer to recognise someone’s face.
According to artificial intelligence expert Krittika D’Silva it involves “teaching a computer to try and recognise parts of the face, by using different characteristics like the eyes and the nose”.
The technology is used for security purposes by some shops, as a way of putting off people who want to steal things.
Krittika D’Silva says we could soon see the technology a lot more in everyday life, including in classrooms, in airports and even as a way of identifying missing children.
“Places where we could expect to see facial recognition in the future are for automatic roll-call in a classroom to see who is and isn’t there, in entrances for businesses and in departure gates in airports to see who is coming through,” she said.
Facial recognition has also been used by police forces across the world in an attempt to catch criminals on high streets.
The Metropolitan Police in London has a database with the faces of criminals its officers want to catch.
Since January 2020, the Met has been using facial recognition cameras in an attempt to spot these faces in and around London.
However, campaign groups have criticised this move, arguing that it’s biased against people from ethnic minority communities.
There have been lots of studies into the effectiveness of the facial recognition technology used by police forces.
In 2019, a group of experts and scientists – including a former Amazon engineer called Anima Anandkumar – wrote to Amazon asking the company to stop selling its facial recognition technology to police forces in the USA.
In the letter, Anandkumar and the other experts argued that the facial recognition wasn’t ready to be used by police because of questions over its effectiveness on people who aren’t white.
“The data that is used to train this technology is heavily biased,” Anandkumar told Newsround.
What does bias mean?
Bias means that someone prefers an idea and possibly does not give equal chance to a different idea. So, in the case of facial recognition, the software leans towards people who are white because it knows more about them.
In 2019, a US government study into facial recognition companies supported this view.
In the study, the facial recognition software of 99 companies was tested.
The technologies struggled to accurately identify African-American and Asian faces compared to white faces.
“That’s because the earliest faces used were celebrities and most of them were white. Even other aspects in terms of age, in terms of women wearing make-up, all of these create biases,” Anandkumar said.
For Krittika D’Silva, the accuracy of any facial recognition technology is completely dependent on what information it is fed.
“When we’ve been teaching it about people’s faces there hasn’t been as much focus on people of colour so the data contains more images of white men and women,” she said.
This issue of racial bias was brought back into the media spotlight again in 2020 following the death of unarmed black man George Floyd in Minneapolis, USA.
After public backlash over the potential use of the technology to identify people in Black Lives Matter protests, Amazon put a ban on police using its facial recognition technology for a year.
Tech companies IBM and Microsoft also said that they would stop selling facial recognition technology to police.
South Wales Police (SWP) was the first force in the United Kingdom to make an arrest using facial recognition technology in 2017, ahead of the Champions League final in Cardiff.
The force is currently facing a legal challenge over its use of the technology.
A spokesperson from SWP told Newsround that there had never been a wrongful arrest as a result of the use of facial recognition and that the ultimate decision on whether to arrest someone has always been made by a human police officer.
The Metropolitan Police has profiled 13,200 people using facial recognition since January 2020.
Of those people, there have been seven mistaken identities, and one successful arrest and prosecution, it said.
A Met Police spokesperson told Newsround that the technology has not been used at any Black Lives Matters protests and that any arrests are ultimately made after being checked by a human police officer.
When we speak of facial recognition, we refer to a particular technology developed for the first time in the 1960s and that has recently come to terms with a truly remarkable impulse.
As it is well known, facial recognition is a technology that has to do with computer security of any kind of device and has become a must, not only for professionals but also, and above all, for all of us.
Biometrics is undoubtedly an important means of ensuring safety. Just think that companies that produce smartphones have long been involved in the manufacture of devices capable of recognizing fingerprints. Fingerprint recognition, however, is a safety procedure that now represents the past, especially if one takes into account the fact that there are numerous solutions to boycott it.
Facial recognition is slowly replacing it. It allows you to keep any kind of data safe, as well as the devices themselves, by simply scanning your face. As you can imagine, the first company to take the first steps in this area was Microsoft with its Windows Hello.
All the users who have Windows 10 will be amazed to know that that they will now be able to turn on their computer simply by smiling in front of their webcam. With regard to facial recognition, it is important to take into account that this is not a new invention. As mentioned, already in the 1960s a series of experiments were carried out, allowing us to lay solid foundations in this field.
How does facial recognition work?
Facial recognition is very simple. To recognize a face, the so-called artificial intelligence algorithms are used to ensure that the recognition is quick and, above all, automatic.
The road to go, however, is still very long. Although science and research have made a great stride forward, the development phase is only at the beginning and, therefore, it is really very difficult to figure out what the future holds in this regard.
For sure, smart phones, PCs, and tablets will not fail to resort to that technology. It is also possible to use facial recognition in other fields such as for example, road safety.
But what is the margin of error of these instruments? Answering this question is not that easy. It may, however, be helpful to try to understand what are the references used for the recognition. Facial recognition can be carried out either by taking into account the distance between the two pupils, the size of the nose and other facial characteristics, or, alternatively, studying the way the so-called pixels combine to give shape to the elements that compose the face. In this second case, you will only proceed with comparing the results obtained with the images stored in the database.
Both techniques are very similar to the mechanism behind fingerprints. Basically, facial elements are considered as the lines that lie on fingers. However, recently, a new technology has been implemented. Even though it still is in the process of improvement, this new technology seems to have the right characteristics to become the most widely used.
This technology should be able to recognize faces through a simple comparison. In all cases, the facial recognition process goes from the detecting phase that is carried out by sensors which have been appropriately installed in a webcam, to the alignment, during which the machine proceeds with the measurements, the representation, the comparison and, finally, with the identification.
- The measurement consists of collecting all the numeric values that are useful to proceed with the calculation of the algorithm.
- The representation, instead, is nothing more than transforming this data into real curves that will serve to translate numbers into a face. With the comparison, then, you have the opportunity to make a real comparison between the images that are stored in the database and those that are received.
- The identification, of course, is the last stage of the process where the face, translated into a code, is recognized.
What is interesting to highlight is that facial recognition, thanks to its accuracy and speed of response that is able to provide, is a highly widespread technique for airport security checks. This demonstrates that significant steps forward have recently been made. Facial recognition has been developed thanks to a large-scale sharing of personal pictures as well as thanks to the spasmodic use of our smartphones.
Among the new frontiers of facial recognition is the interpretation of emotions
In this case, facial recognition can be useful for doctors to detect various types of illnesses including depression, for example.
Ethical issues are not lacking since facial recognition seems to be a mechanism that, if used improperly, could violate our personal freedom. In any case, at least for the moment, this is a more than lawful solution besides being perfect for meeting the constant need for security in every area of our lives. When it comes to facial recognition, it is impossible not to think of home automation. Indeed, home automation could benefit from this new technology and we are very close to seeing the first experiments.
Basically, facial recognition is nothing more than a further step forward in research that, although putting pressure on the traditional privacy system, guarantees security and allows us to maximize our time in daily actions. There is nothing else to do then wait and see what will be the legal measures that will be taken in this respect.
Meanwhile, thinking about realities told in films such as “Minority Report” or in the great TV-show “Person of Interest” might be useful to better understand what could be the real applications of the technology that is behind facial recognition.
Facial recognition is a technology that identifies and verifies a human face by analyzing the facial features of an individual through images, video or in real-time and matching the information with a database. It is a biometric identification process in which computer algorithms are used to match specific facial patterns such as distance between the eyes or shape of the chin, etc. The information is then converted into a mathematical formula and compared to the faces in the database to authenticate an individual.
The technology used can vary but the basic functioning remains the same across systems. Here’s how it works:
Step 1: Face detection
A camera captures the image of a face of an individual from a photo or a video or in real-time. The individual could be alone or part of a crowd. Ideally, in the image, the individual looks straight ahead. However, technological advancements allow the software to work even with faces turned in a different angle.
Step 2: Face analysis
The facial recognition software reads the contours of the captured face. Most of the software prefer 2D images instead of 3D since they are easier to match with those in the database. It then identifies different facial landmarks — also known as nodal points — such as distance between the eyebrows, shape of the nose or the cheekbones, etc.
Step 3: Converting the image into data
Once the face has been analyzed, the information is turned into a mathematical formula with each nodal point becoming a number code. All the codes are together fed into the database as a unique faceprint, similar to a unique thumbprint.
Step 4: Finding a Match
The faceprint is then compared with millions of images from the database. The software tries to match the image with one having the exact facial features. If a match is found, then the user is authenticated with relevant information such as name, address, etc.
Purpose of facial recognition
Facial recognition is generally used for an application, a system or a service. It is vastly used for security by the police to identify miscreants. However, currently, it is used for multiple other purposes, such as:
- Device security: Unlocking apps and smartphones
- Airlines: Identification by customs; biometric check-in by airlines
- School security: Attendance in class and during exams; as a security shield against offenders
- Shoplifting: Identifying shoplifters and marking them as threats
- Entertainment: Making funny filters like how one would look in old age, etc.
- Marketing: Spotting target groups based on gender, age, ethnicity, etc. to push a product
Facial recognition is here to stay for the long haul and we are still at the tip of the iceberg. The technology is growing by leaps and bounds with multiple upgrades to make it more reliable and secure. It is just a matter of time until facial recognition becomes a part of our daily lives.
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Facial recognition is a technical way of identifying a human face. It uses biometrics to scan the facial demography of a person. After taking the essential biometrics, it compares the information with a database of known faces to find a match. Facial recognition works to verify personal identity but it also brings many privacy issues if used for evil purposes. The facial recognition market is expected to grow to $7.7 billion by 2022. It was just $4 billion in 2017. The reason behind the growth is its wide use in many places.
How Facial Recognition Works
Facial recognition works through a facial recognition software that holds a massive amount of data. This data can be easily stored and accessed. Half of all American adults have uploaded their faces in many facial recognition databases. There are some steps that a user needs to follow on the software.
- Use a picture of your face from a photo or video. Your face must be alone and clear. Your photo also needs to be looking straight.
- Upload the photo on the software so that it can read the geometry of your face. The geometry includes the distance between your eyes and distance from forehead to chin. The software recognizes all the facial landmarks to distinguish your face.
- Further, the software checks your facial signature which is a mathematical formula. The formula is then compared to a database of known faces.
- A final determination is made to bring a faceprint that matches with an image in a facial recognition system database.
Where it is Used
Many governments at airports are using the face search engine for finding lookalike faces and to monitor people coming and going to airports. It helps agencies to check which person has overstayed their visas and who may be under criminal investigation. Mobile phone makers are also using facial recognition to lock and unlock the phones. First of all, Apple had started to use this technology. Now almost every mobile maker is launching phones with face recognition facility. Businesses are also using this technology at entrances and restricted areas.
What is photo face recognition technology?
The main parts of any face like the eyes, forehead, nose, chin, and cheekbones are always located in the same places in relation to one another. The distance between the corners of the eyes and the lines of the pupils and the chin, for example, always remain unchanged in adults even with age, or when they are tired, drunk, or sick. It is therefore possible to draw up a mathematical «formula» for each person’s face and compare it with the formulas of others.
Dedicated professional facial recognition solutions can identify criminals from surveillance camera footage among crowds of thousands. Moreover, the comparison of a «live» image with a photo from
a database occurs in real time.
However, an OSINT investigator’s task is different. They have to find all the available information related to an individual in social networks, including profiles, accounts, internet addresses etc. based on a mere photo. The problem is that the photos may not match in different networks or may be years apart.
Social Links transforms for Maltego allow photo identification in two different ways. The first approach assumes that the name of the person in the photo is known. It is then possible to find all the accounts of that individual in many social networks. The other method is applicable if there are several photos of the same person. Then it is possible to compare another image with them and find out whether it is the same person or not.
How does an image or photo facial recognition system work?
Though the approaches to organizing the search of individuals online may vary, the base steps of the process remain the same with some editing.
First, a photo of the individual of interest is procured. The angle of the face is irrelevant, as is its location, as long as the key facial metrics are visible.
Then, the facial recognition software analyzes the face’s geometry and builds a so-called facial signature based on a number of key parameters.
Once the facial signature is calculated, it is compared with a database of faces. Governments have access to millions of such signatures. The final step is either a match or a no-match if the face is not in the database.
What is photo facial recognition used for?
The most obvious application for face recognition software is law enforcement and government authorities that are responsible for management of millions of people’s records and maintaining order. However, the software has permeated into other industries.
Security is one of the applications for such software. For instance, Apple uses Face ID as an instrument for locking phones and prohibiting access. The software has its flaws, but the odds of random face access to one’s phone at 1 in a million are good enough for most consumers. Another security application for the solution is in colleges and universities to restrict access to unauthorized individuals during exams or as screening at the entrance to prevent terrorism or crimes. The same applies to corporate buildings, airports and other premises with high security levels. In some cases, even churches have been known to use facial recognition to check congregation attendance for calculating donation tabs.
Social networks are among the leading users of the software. Facebook has its own algorithms for identifying people’s faces in photos and automatically linking them to their profiles with over 98% accuracy.
Businesses are also leading the way in scanning faces to identify potential audiences for age, gender and other parameters that would allow them to make sales of specific products or services.
However, for OSINT investigations, the priority tasks are still establishing all the connections of a person in social networks and finding their alias.
Face recognition and image search software for photos from Social Links
Social Links searches by photo transforms for Maltego are performed in Facebook, Instagram, LinkedIn, Myspace, OK, Twitter, VK, and Xing. This function always evokes awe among audiences at any demonstration due to its uniqueness.
Based on a person’s photo, any of their accounts in any of the aforementioned social networks can be found if they are open. If viewing is restricted “for friends” or otherwise, the function will not work. But the person can still be identified even if the photos in different social networks do not match.
For the practical purposes of OSINT investigators, it is important to be able to automatically work with a large set of photos. This is exactly how SL transforms work as hundreds of photo-name pairs are searched simultaneously. The result may not be instant, but the efficiency is high and outstrips any manual verification.
Another vital task is determining whether the same face is depicted in different photos. The automated products from Social Links allow comparing a photo with a set of existing images and establishing the probability of whether the person in the new photo is the same as the one in the database. The functionality works with images of different formats and is versatile enough to recognize faces at different angles and resolutions.
The software also works in reverse in cases when there is a database with thousands of photos and the image of a stranger has to be identified among them. The solution from Social Links can establish the likelihood of a match simultaneously for thousands of photos.
1 August 2019 | Written by La redazione
Detecting a face it’s easy, recognizing one it’s way more complex
Facial recognition is everywhere, we find it in our phones and computers but also used by government agencies for different purposes. This is a field of study, that of facial recognition, which has seen enormous development in recent years and which in the future will play an increasingly important role in the intertwining of society and technology. But how do machines recognize our faces?
How facial recognition works. Recognizing a face is not a simple task. We humans got used to it because we learn it from birth but it can be problematic for a machine. To be able to recognize a face it is necessary first of all to detect it, therefore finding in the images those fundamental parts that characterize a face: two eyes, a nose, a mouth. So far it is not too complex, it is the distinction between two faces that requires a completely different approach.
Every single detail counts: for this reason the facial recognition algorithms are trained to detect details in the order of millimeters of many parameters: from the distance between the two pupils at the depth of the orbits, passing through the height of the cheekbones, the shape of the jaw, the width of the nose and other dozens and dozens of attributes.
Risks and abuses. The most effective facial recognition systems can distinguish two identical twins and have a success rate of almost 100%. A powerful technology, therefore, which causes a lot of concerns: for this reason several States have decided to prohibit their use by the police, who risked abusing this technology, not yet able to offer sufficient efficacy guarantees.
It should also be considered that often algorithms that manage facial recognition are trained on databases composed of images of white men, this means that the accuracy of the system is lower when it has to recognize people belonging to ethnic minorities, such as for example African-Americans, which makes the system easy to fall prey to racist results.
Facial recognition software is becoming increasingly popular across a wide range of industries and experts predict it will continue to become more integrated into business processes in the future.
While once only used by a handful of intelligence and security agencies, facial recognition has become a part of daily life for many individuals and businesses. It has taken on many applications—from unlocking phones to identifying people in social media photo posts to verifying identity for top level security clearance.
But how exactly does face authentication software work? And how can companies leverage it to their advantage?
Researchers and tech experts began experimenting with using computers to recognize the human face in the 1960’s. Over the past sixty years, facial authentication technology has evolved significantly, becoming incredibly precise and accurate.
Today, face recognition software can map someone’s facial features and verify their identity using a photo, a video, or a live interaction. It does so by scanning the geometry of a face and interpreting the data as a mathematical formula which can then be compared against other formulas in the system. Some of the elements measured include the distance between a person’s eyes, the distance from their chin to forehead, and the texture of their skin.
Face match software uses artificial neural networks to process data—a type of artificial intelligence known as “deep learning.” Deep learning algorithms mirror how a human brain learns information, utilizing increasingly sophisticated layers of processing that allow the system to extract higher level features from data. Because of this, face matching technology can have near perfect accuracy in ideal conditions and reduces the risk of fraud significantly.
The Federal Trade Commission received 4.8 million identity fraud reports in 2020 and more than 150 million people were affected by data breaches. It is becoming more important than ever for companies and individuals to protect their assets using the most secure technology available.
PRUVID has recognized the critical role that face authentication and artificial intelligence will play in helping individuals, businesses, and governmental organizations streamline their processes and improve security. Our live video face authentication and photo verification are the gold standard in virtual authentication with a 98 percent accuracy rate, eliminating the need to carry ID and securing sensitive data and documents. This state-of-the-art software has numerous business applications, from background checks to onboarding to digital signatures. It can even verify the identity of an individual wearing a face mask.
To learn more about PRUVID’s software and services, contact one of our representatives today.
Facial Recognition System
The facial recognition is nothing but the technology that identifies human face by photo or video. The facial recognition system uses bio metric data of whole face to identify the person by comparing it to ready database.
The face recognition technology now comes in your smartphone. You can now unlock your phone by just having a glance at your smartphone. This is possible only due to advancement in this technology.
The use of such technology also creates privacy concerns. The data here is nothing but your face and here comes the real glitch. The misuse of such data can have devastating effects on society. And so, people are really concerned about this technology.
How Facial Recognition works?
Every day we come across different faces in society such as friends, family members and colleagues. All have a variety of facial features such as eyes, nose, chin, cheeks and even ears. We identify everyone by comparing the faces we see to faces we store in our memory. In exactly the same way, facial recognition works.
In facial recognition system, all the above processes are done by a number of algorithms by a computer. It also needs a large database to store all the facial features data in order to access it later as and when required.
First of all, the face is captured from photo or video. The system can even capture the face in crowds as well. The facial recognition software then reads the geometry of your face. This data includes the distance between the eyes or between the chin and forehead. In addition, some of the software can identify other facial features. Even the minute details can also be recognized by the system. As a result, it creates the facial signature of the captured face.
The facial signature is nothing but a mathematical formula which is then compared to data sets in a vast database of faces. The software compares the data and once it identifies the person, it shows more information about the captured image. This information may include the name of the person, date of birth, gender etc .
Techniques to acquire and identify faces
The facial recognition involves a lot of techniques in order to acquire and process facial data. The traditional technique involves algorithm and data sets of faces. The Algorithm compares the data with a ready database and gives results.
Furthermore, the other technique is skin texture analysis. The analysis involves analyzing data related to the skin including lines, pores, spots on the skin and converting that data into the mathematical model.
Another approach to acquiring the face is 3-dimensional facial recognition. In this technique, 3D sensors acquire information about the shape of the face. The system specifically identifies the surface of the face including chin, eye sockets, and nose. This technique has more advantage over others. The main reason behind the advantage is the ability to identify the face from different viewing angles. So, it’s simply more precise and accurate as you get three-dimensional facial data.
In addition, more recent use of thermal cameras in facial recognition is an emerging technique. In this technique, the thermal camera captures the shape of the head and ignores the other part of the face. Most importantly thermal cameras can acquire the subject in very low-light or at night time without exposing itself. Although the real concern here is the availability of thermal picture databases for identification.
Applications of Facial Recognition
The facial recognition systems now have lots of importance. The most important beneficiary of the system is the law enforcement agency. Around the world, facial recognition helps to track down persons in the most-wanted list.
The use of this technology will soon become a norm on airports worldwide , for border crossing and for national security. The accuracy of this system is now more reliable. Many governments around the world are trying to enforce it during voting in order to avoid fake voting.
In addition, the use of this technology now getting more importance in the corporate sector, as it helps to track the attendance of the employee. This technology also helps to prevent duplication or fake identity cards, passports, and many other identification documents.
Furthermore, in recent years, facial recognition becomes widespread in smartphones specifically for unlocking the smartphone. Even some social media networks started using it to identify faces in photos uploaded by users.
Advantages and disadvantages of Facial Recognition systems
The important advantages of facial recognition is it’s accuracy and reliability when we compare it to other biometric options. The system is particularly helpful in identifying faces in large crowds at public places. The identification via photos and videos stand strong legally. Hence, it is more popular with security and law enforcement agencies.
Furthermore, the recognition systems have disadvantages of their own. As these systems need face information and it also needs to be stored in databases which leads to privacy concerns. More and more use of this technology may also lead to identity theft and possible frauds.
People around the world are now very cautious about the privacy. So, this also leads to slow implementation pace of such systems. There is strong fear among the people about the misuse of this technology as it may ruin someone’s life.
In conclusion, the facial recognition systems are still in the nascent stage. However, they will improve in the near future, mostly due to large scale developments in artificial intelligence.
Image courtesy: Cisco
Watch facial recognition in action
How easy is it to identify a person you have met before? Usually, very easy! You may say it is because you remember him. Technically, your brain is matching it against a memory it has. Face recognition by machines work in a similar fascinating way.
What is Face Recognition?
The Dictionary definition of face recognition is the ability of a computer to scan, store, and recognize human faces for use in identifying people.
In 2020, this term is not very alien. Apple users use Face ID to unlock their phones. Airport security uses it to identify convicts. Workplace safety apps like Truein use it to mark attendance or authorize visitors.
Steps for Face Recognition
There are many variations of this technology. We will look at the most common approach. Let’s break down the definition.
Scan: A person walks by a machine that takes its picture and identifies its geometry. By geometry we mean distance of the nose from chin or location of eyes. There can be 60 to 80 such data points that make the complete face ID.
Store: The face ID, a mathematical formula consisting of data points is stored in the database. The computer understands this as your signature – unique to you.
Recognize: Next time you walk past the same machine, it can create a similar map and trace it against the database it has. Result? You are identified.
Not all face recognition technology is created equal
Face recognition was first invented by Woodrow Wilson Bledsoe in 1960s. Back then the degree of errors was vast and the amount of time it took to return a result was huge. Part of it was due to computing power of machines back then. Besides, the number of facial markers used to create a Face ID were low. In 1970s there were 21 facial markers. Today, the sophisticated machines use as many as 80, increasing accuracy.
Facial recognition gets seriously compromised if the machine is not geared to identify in low-light or with changing facial features. It will result in two things:
- False negative: It won’t identify a person despite his ID being present in the database. It can be frustrating, but it is not dangerous.
- False positive: It will identify a person wrongly as another person. A false match is a bad sign, especially when law-enforcing agencies use it.
It is therefore critical that you pick a face recognition technology that stands the test of time. A time-tested and proven technology by a trusted provider is the key to adopting this futuristic technology.
What makes Truein’s face recognition technology unique?
We believe that accuracy has to be practically hundred percent. Any small error leads to manual work, beating the purpose of our technology. This hundred percent perfection has happened over many years through many iterations. Today, we are proud to meet those benchmarks.
- Truein face recognition attendance is benchmarked with eyes. Result? No amount of facial changes can affect our performance. Ageing, hats, face masks – doesn’t matter. It works. We beat the odds.
- Our face recognition technology has proved to work in low-light conditions as well. So common work conditions like night shift workers or outdoor construction sites don’t pose a challenge to our system.
- It works offline too. So, there are practically no barriers to primary enterprise activities – face attendance and visitor management. Distant sites or stores too can use it seamlessly.
- We have invested in the necessary infrastructure to make it super-fast. We surely don’t want people to wait in queues while the device scans and marks face attendance for each employee. It takes just three seconds, including changeover time.
Why face recognition is the most futuristic ID system?
Biometric IDs are unique and beat the traditional paper-pen or card systems hands down. Cards can be swiped by proxies and attendance can be fudged. It is a common malpractice, often seen at plants within contract workers.
The thumb IDs are biometric too, but they have failed to meet the hundred percent benchmark. Women using Indian Mehndi on hands feel frustrated when their thumbs don’t get identified at office machines. Similarly, plant workers who work on oily machines cannot walk past a thumb-based system easily. Besides, it is not touchless, and it takes multiple attempts for one correct match.
Face recognition is truly the only unique identifier, doing its job in few seconds. It cannot be proxies or scope for manual overrides.
Any futurists among you, sci-fi fans, or those with a curious nature may have been exposed to fictional impressions of facial recognition software. Sounds like technology that’s far, far away in the future, right? Well, believe it or not facial recognition software has been commonplace for almost a decade.
In 2001, the Tampa Police Department installed police cameras, equipped with facial recognition technology in the Ybor City nightlife district in an attempt to combat the growing crime rate. Sadly, after a two year experiment the software was deemed ineffective, and the project scrapped, yet with perseverance this technology has become prominent in today’s society.
Based in Minnesota, Identix is one of many companies that are presently developing facial recognition software for mainstream use. The Facelt software has the ability to pick out an individual face from a crowd and run a comparison search with a database of stored images.
So, how exactly does this software work?
Every individual face has its own distinguishing marks and features. From peaks and valleys that make up the contours of our face (or nodal points) to distinguishing features, each face is different. Each human face has approximately eighty nodal points. The software measures these through the following: the distance between the eyes, the width of the nose, the depth of the eye sockets, the shape of the cheekbones, and the length of the jaw line. These nodal points are measured creating a numerical code, called a face print. The image is then uploaded onto a database.
Facial recognition software of the past has relied on a 2D image for comparison. In order to be effective the image needs to be ideally looking directly into the camera with little variance in light or facial expression, obviously the margin for error was very slight. Even the smallest change would result in a nullified image.
3D Facial Recognition Software
As facial recognition software has evolved 3D images have become prevalent. Capturing a real-time image, with a person’s distinctive facial features like a rigid tissue and bone structure, precise eye socket curvature, and chin prominence has become possible. These features do not change over time, and are unique to the individual.
What is the exact science of 3D Facial Recognition Software work?
Firstly, the image is detected by digitally scanning an existing 2D photograph or by using a video image to provide a live picture of the subject.
Secondly, once the software has detected a face, the system determines the position of the head and the measurements of the features. The 3D software has the potential to recognise a face up to ninety degrees, whereas 2D software requires the face to be at least thirty five degrees from the camera. The software will then create measure the curves of the face on a sub-millimetre scale and create a template.
Thirdly, once the template has been established the software will then translate the template into a unique code. The code gives the template a set of numbers to represent the facial features. Once this process is determined the image is then scanned into a database containing a wealth of 3D images to determine a match. The challenge comes from the fact that some databases still only recognise 2D images.
The final step is to verify the image. The image should be solely matched to one image in the database. The match that’s identified in the database is called ‘Image 1.1’.
This software is especially prevalent and useful for law enforcement agencies, as well as a number of other sectors. Facial recognition software is here, and proving to be a valuable asset for society.
- WindowsВ 10
- Windows 11
Windows Hello is the biometric authentication feature that helps strengthen authentication and helps to guard against potential spoofing through fingerprint matching and facial recognition.
When Windows 10 first shipped, it included Microsoft Passport and Windows Hello, which worked together to provide multi-factor authentication. To simplify deployment and improve supportability, Microsoft has combined these technologies into a single solution under the Windows Hello name. Customers who have already deployed these technologies will not experience any change in functionality. Customers who have yet to evaluate Windows Hello will find it easier to deploy due to simplified policies, documentation, and semantics.
Because we realize your employees are going to want to use this new technology in your enterprise, we’ve been actively working with the device manufacturers to create strict design and performance recommendations that help to ensure that you can more confidently introduce Windows Hello biometrics into your organization.
How does Windows Hello work?
Windows Hello lets your employees use fingerprint or facial recognition as an alternative method to unlocking a device. With Windows Hello, authentication happens when the employee provides his or her unique biometric identifier while accessing the device-specific Windows Hello credentials.
The Windows Hello authenticator works to authenticate and allow employees onto your enterprise network. Authentication doesn’t roam among devices, isn’t shared with a server, and can’t easily be extracted from a device. If multiple employees share a device, each employee will use his or her own biometric data on the device.
Why should I let my employees use Windows Hello?
Windows Hello provides many benefits, including:
It helps to strengthen your protections against credential theft. Because an attacker must have both the device and the biometric info or PIN, it’s much more difficult to gain access without the employee’s knowledge.
Employees get a simple authentication method (backed up with a PIN) that’s always with them, so there’s nothing to lose. No more forgetting passwords!
Support for Windows Hello is built into the operating system so you can add additional biometric devices and polices as part of a coordinated rollout or to individual employees or groups using Group Policy or Mobile Device Management (MDM) configurations service provider (CSP) policies.
For more info about the available Group Policies and MDM CSPs, see the Implement Windows Hello for Business in your organization topic.
Where is Windows Hello data stored?
The biometric data used to support Windows Hello is stored on the local device only. It doesn’t roam and is never sent to external devices or servers. This separation helps to stop potential attackers by providing no single collection point that an attacker could potentially compromise to steal biometric data. Additionally, even if an attacker was actually able to get the biometric data from a device, it cannot be converted back into a raw biometric sample that could be recognized by the biometric sensor.
Each sensor on a device will have its own biometric database file where template data is stored. Each database has a unique, randomly generated key that is encrypted to the system. The template data for the sensor will be encrypted with this per-database key using AES with CBC chaining mode. The hash is SHA256. Some fingerprint sensors have the capability to complete matching on the fingerprint sensor module instead of in the OS. These sensors will store biometric data on the fingerprint module instead of in the database file.
Has Microsoft set any device requirements for Windows Hello?
We’ve been working with the device manufacturers to help ensure a high-level of performance and protection is met by each sensor and device, based on these requirements:
False Accept Rate (FAR). Represents the instance a biometric identification solution verifies an unauthorized person. This is normally represented as a ratio of number of instances in a given population size, for example 1 in 100 000. This can also be represented as a percentage of occurrence, for example, 0.001%. This measurement is heavily considered the most important with regard to the security of the biometric algorithm.
False Reject Rate (FRR). Represents the instances a biometric identification solution fails to verify an authorized person correctly. Usually represented as a percentage, the sum of the True Accept Rate and False Reject Rate is 1. Can be with or without anti-spoofing or liveness detection.
Fingerprint sensor requirements
To allow fingerprint matching, you must have devices with fingerprint sensors and software. Fingerprint sensors, or sensors that use an employee’s unique fingerprint as an alternative log on option, can be touch sensors (large area or small area) or swipe sensors. Each type of sensor has its own set of detailed requirements that must be implemented by the manufacturer, but all of the sensors must include anti-spoofing measures (required).
Acceptable performance range for small to large size touch sensors
Police forces around the UK are coming under fire for their trials of live facial recognition technology.
What is live facial recognition?
Live facial recognition (LFR), also known as automatic facial recognition, identifies people in a video in real time, using a set of photographs as a reference. When used in public, cameras scan a crowd and the software highlights any matches between members of the public and the people in their database.
How does live facial recognition work?
The live video feed is scanned for faces. Each face that is found is then mapped by the software, taking measurements of facial features, such as the distance between the eyes and the length of the jawline, to create a unique set of biometric data. This dataset is then compared to a database of people to be identified; for the police, this database contains people with outstanding warrants. If the system judges the face to be sufficiently similar to someone in its database, this match is highlighted.
Who is using live facial recognition?
In the UK, the London Metropolitan Police (the Met), the South Wales Police and Leicestershire Police have all trialled the technology in public since 2015. The system was tested at Download Festival in 2015, the Champions League final and Notting Hill Carnival in 2017, among other events, with the Met’s final test taking place on 14 February 2019.
In these cases, the database consists of photos of people wanted by the police or courts. If the system makes a match, it presents the police with both images, so they can decide whether to stop and speak to the person. Unmatched faces are deleted straight away, and matched images are deleted after 30 days.
Read more about security with technology:
Other facial recognition systems are already in use in the UK. In 2004, EU countries began to incorporate biometric data into new ePassports, identifiable by a small, gold camera logo on the front cover. These became available in the UK in 2006, and the microchip embedded in the cover contains both the holder’s personal information and photograph.
In many airports across the UK and Europe, as well as Eurostar terminals in Paris and Brussels, travellers can verify their identity with the facial recognition systems in automated ePassport gates in immigration halls.
Trials of facial recognition software were also carried out in three prisons in an attempt to combat drug smuggling. HMP Hull saw a 40 per cent drop in visitors during this time, and a spokesperson for the Ministry of Justice described this as a successful deterrent.
Amazon’s facial recognition system, Rekognition, has been tested by police forces in the US, including Orlando Police Department in Florida and Washington County Sheriff’s Office in Oregon. It was reported in July that the system had been abandoned in Orlando after 15 months of unsuccessful trials. The FBI and Immigration and Customs Enforcement (ICE) have also used the technology to identify undocumented immigrants from their driving licence photographs.
Is live facial recognition legal?
Currently, there is no UK regulation regarding facial recognition technology. Though the police trials have been supported by Home Secretary Sajid Javid, he told the BBC that legislation would have to be put in place before it could be used long-term.
The House of Commons Science and Technology Committee published a report in July calling for a moratorium on all facial recognition technology until legislation has been put in place.
Meanwhile, San Francisco and Oakland in California, and Somerville in Massachusetts, have all banned the use of the technology.
How effective is facial recognition?
The issues raised by the committee’s report include the technology’s effectiveness. For example, at the 2017 Champions League final, the facial recognition system highlighted 2,470 matches to individuals on the police’s database. Only 173 of these were correct, giving it a 92 per cent error rate. When the Home Office tested the Police National Database’s system in 2015, they found that it was half as effective as a human, and a University of Essex study of the Met’s system found it to be correct only 19 per cent of the time.
Facial recognition systems also have the potential to be biased. If the database that ‘trains’ them is predominantly white and male, it will be much more effective at distinguishing white, male faces. The risk is that women and ethnic minorities are much more likely to be falsely identified. A 2019 paper from MIT found that Amazon’s Rekognition had a 0 per cent error rate among light-skinned men, which rose to over 30 per cent for dark-skinned women.
Can we stop our biometric data being collected?
The Met’s website says that “Anyone can refuse to be scanned; it’s not an offence or considered ‘obstruction’ to actively avoid being scanned.” However, the BBC reported in May that one man was fined £90 for disorderly conduct after attempting to cover his face during a trial of the system in East London.