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.
Facial recognition technology has sufficiently matured recently and its use has been rapidly increasing both in commercial products, as well as by law enforcement agencies. NTechLab definitely has your face if you live in Russia, as the company claimed in 2017 they could track every user on VK, and can search a database of a billion faces in half a second.
The impact of COVID-19 pandemic differed from region to region across the world. The facial recognition attendance software is a real-time and contactless attendance tracking software useful in the current pandemic. Businesses are resuming their on-premises operations to ensure continuous business operation and under such health crisis, employee health and safety are of paramount importance.
The coronavirus reality motivated China’s technology giants such as SenseTime and Minivision to rush headlong into the commercial deployment of mechanisms for recognizing faces in such scenarios. Not only can the new algorithms identify people with masks on their faces, but they can also pinpoint those wearing scarves, glasses, caps, and fake beards.
Facial recognition is also a powerful instrument for law enforcement agencies to track down criminals, and for governments to surveil their citizens. It is a stronghold of the Skynet mass monitoring system deployed in China, with more than 600 million cameras having been installed across the country.
The benefits of facial recognition systems for policing are evident: detection and prevention of crime. “Governments and corporations around the world today face security threats everywhere – within the organization’s facilities, on the street, in the face of threats of damage and theft and of course in the face of national security threats. Facial recognition is a key tool for dealing with these threats,” Former Mossad chief and director of Tokyo-based holding company SoftBank’s Israel operations Yossi Cohen said at a conference for AI company Oosto in Raanana. He explained that although biometric identification using fingerprints is accurate, it requires the cooperation of the suspect, who is likely aware of the technology. “When the collection is done physically – it just does not work,” he added.
Facial recognition is used when issuing identity documents and, most often combined with other biometric technologies such as fingerprints (prevention of ID fraud and identity theft). In light of the COVID-19 outbreak, the tech is superseding tickets as a touchless way to enter stadiums and enjoy sporting events in New York and Los Angeles. Since more people are wearing surgical masks and respirators when outdoors, facial recognition and video monitoring systems needed new advancements to tackle this challenge.
Face match is used at border checks to compare the portrait on a digitized biometric passport with the holder’s face. In 2017, Thales was responsible for supplying the new automated control gates for the PARAFE system (Automated Fast Track Crossing at External Borders) at Roissy Charles de Gaulle airport in Paris. This solution has been devised to facilitate evolution from fingerprint recognition to facial recognition during 2018.
Face biometrics can also be employed in police checks, although its use is rigorously controlled in Europe. In 2016, the “man in the hat” responsible for the Brussels terror attacks was identified thanks to FBI facial recognition software. The South Wales Police implemented it at the UEFA Champions League Final in 2017.
In the United States, 26 states (and probably as many as 30) allow law enforcement to run searches against their databases of driver’s license and ID photos. The FBI has access to driver’s license photos of 18 states.
Drones combined with aerial cameras offer an interesting combination for facial recognition applied to large areas during mass events, for example. According to the Keesing Journal of Documents and Identity of June 2018, some hovering drone systems can carry a 10-kilo camera lens that can identify a suspect from 800 meters from a height of 100 meters. As the drone can be connected to the ground via a power cable, it has an unlimited power supply. The communication to ground control can’t be intercepted as it also uses a cable.
Facial recognition CCTV systems can improve performance in carrying public security missions. four examples are to Find missing children and disoriented adults , Identify and find exploited children , Identify and track criminals , and Support and accelerate investigations.
Facial recognition application in schools
In the wake of deadly school shootings throughout the country, Western New York districts are considering enhanced security measures in their buildings, and now, Maryvale could be the latest area school to implement cutting-edge — and controversial — surveillance system with the ability to spot specific faces and weapons out of real-time video feeds.
Orchard Park-based consultant Tony Olivo, pitched Maryvale on the Aegis system, a three-pronged security suite combining facial- and shape-recognition tools with a forensic search engine. According to Olivo, the system is one-of-a-kind, developed by a Canadian company, SN Technologies, and built up from counterterrorism technology that’s been used at the highest levels in Europe by agencies such as Scotland Yard and Interpol.
“The foundation of this technology has been used by governmental entities throughout the world. … We have worked to, let’s say, tweak it or develop it for applications in schools and hospitals, but it’s the same development team and everything else. It’s just a different application.”
Basically, Aegis works like this: Districts load images of people, such as those on state sex offender lists, into a forensic search engine, known as Mercury. Aegis works with the schools’ cameras to scan for faces, using a tool SN Technologies calls Sentry, that matches those on the prohibited lists.
If the system makes a match, an alert is sent to a person in the district who can verify the hit. In other words, after the system thinks someone is in the school who is not supposed to be, a human confirms it and then can decide on a course of action such has locking doors, alerting the police or confronting the apparent intruder.
Olivo noted that the system does not actually record video and that information is not stored — unless the system hits a match. School districts do have policies in place already regarding the shelf life of security camera footage. Additionally, another tool in the system, known as Protector, scans for the so-called top 10 guns used in school shootings and alerts the human monitor if the gun enters school grounds.
“Normal facial recognition utilizes mathematics; it’s the distance between the eyes and the nose, the nose and the mouth and those type of things. You have to have a fairly good shot of a face, good lighting, and those types of things, to get a good match,” Olivo told Maryvale board members. “[The Aegis system] is based upon neuroscience, as opposed to computer science. It’s based upon shape and pattern recognition and biometrics, the way the eye sees.” This makes the technology much more precise, Olivo said.
Facial Recognition being used by counter-terrorism agencies and Military
The FBI is piloting Amazon’s facial matching software—Amazon Rekognition—as a means to sift through mountains of video surveillance footage the agency routinely collects during investigations. The pilot kicked off in early 2018 following a string of high-profile counterterrorism investigations that tested the limits of the FBI’s technological capabilities, according to FBI officials.
China is reportedly testing a sophisticated facial recognition system that could closely monitor targeted people in a Muslim-dominant province. The network is installed at residents’ homes and workplaces in the Xinjiang Autonomous Region in western China, reported Bloomberg. The new face-reading AI technology would alert the authorities if any suspects leave more than 300 meters (984 feet) beyond the designated ‘safe areas’, said the Bloomberg report quoting an anonymous insider. Police officers use a camera attached to sunglasses and connected mobile device that the police officers carry that contains offline face data, allowing the system to work quickly. According to the Journal, at one city’s railway station, they’ve nabbed seven people associated with crimes using this method, as well as others traveling under false identities.
China is also setting up and perfecting a video surveillance network countrywide. Over 200 million surveillance cameras were in use at the end of 2018, and 626 million are expected by 2020. The facial recognition towers in Chinese cities are emblematic of this move. This is linked to the social credit system the Chinese government is developing. In the TOP 10 cities with most street cameras per person, Chongqing, Shenzhen, Shanghai, Tianjin, and Ji’nan are leading the pack. London is #6 and Atlanta #10, according to the Guardian of 2 December 2019. Chinese police are working with artificial intelligence companies such as Yitu, Megvii, SenseTime, and CloudWalk, according to The New York Times of 14 April 2019.
Philippines Defense Secretary Delfin Lorenzana has revealed government plans to boost the country’s technological capabilities as part of counterterrorism efforts. “We are looking at facial-recognition software so that we can easily track down the bad guys,” Lorenzana said in an interview. International security consultant Stephen Cutler praised the Philippine defense department’s plan to upgrade its tech capabilities. Facial-recognition systems were being used in other countries and were highly effective, he said.
“Say they get pictures of these (militants) with Daesh flags. Even if they’re wearing a bandana across their nose and lower face, facial recognition could theoretically allow us (to identify them). If those guys have already been arrested, we could run a still photo of that camp in the picture (or video) and figure out who’s in the camp.”
Using computers to recognize people’s faces and validate their identities can streamline access control for secure corporate and government buildings or devices. Perth’s new $1.6 billion venue CBD has attracted worldwide attention since opening last month, and is widely promoted as the ‘best stadium in the southern hemisphere’ VenuesWest had considered using the facial recognition technology at Optus Stadium as part of its broader security plan to protect patrons from a possible terror attack. “We have the technical capability to do that at Optus Stadium, it would just take some software and a few little changes,” VenuesWest chief executive David Etherton told the inquiry. He said other major venues like the SCG also had the CCTV infrastructure to use such technology.
Some systems can identify known or suspected criminals. Face recognition CCTV can be used to enable police to track and identify past criminals suspected of perpetrating an additional infraction. Police can also take preventive actions. By using an image of a known criminal from a video or an external picture (or a database), operators can use to detect matches in live video and react before it’s too late. Facial recognition CCTV systems can be used to support investigators searching for video evidence in the aftermath of an incident.
The ability to isolate the appearances of suspects and individuals is critical for accelerating investigators’ review of video evidence for relevant details. They can better understand how situations developed.
US new executive order calls for expansion of facial recognition systems in major U.S. airports to monitor people leaving the U.S., in hopes of catching people who have overstayed their visas or are wanted in criminal investigations. The US military used facial recognition to identify slain al Qaeda leader Osama bin Laden, according to a Reuters report.
Army researchers have developed an artificial intelligence and machine learning technique that produces a visible face image from a thermal image of a person’s face captured in low-light or nighttime conditions. This development could lead to enhanced real-time biometrics and post-mission forensic analysis for covert nighttime operations.
Facial recognition technology
By definition, facial recognition refers to the technology capable of identifying or verifying a subject through an image, video, or any audiovisual element of his face. Facial recognition is the process of identifying or verifying the identity of a person using their face. It captures, analyzes, and compares patterns based on the person’s facial details.
The newly developed facial recognition systems use advanced AI features, IP cameras, machine learning algorithms, and cognitive technology to verify a person from a video source or a digital image. First, the image is automatically processed to identify what is and is not a face.
Next, the system extracts features from each image of a face. The raw image data is processed into a smaller set of numbers that summarize the differentiating features of a face. This is often called a “faceprint.” The face detection process is an essential step as it detects and locates human faces in images and videos. The face capture process transforms analogue information (a face) into a set of digital information (data) based on the person’s facial features. The face match process verifies if two faces belong to the same person. Today it’s considered to be the most natural of all biometric measurements. Identification answers the question: “Who are you?” Authentication answers the question: “Are you really who you say you are?”
The objective of face recognition is, from the incoming image, to find a series of data of the same face in a set of training images in a database. 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. A computer can compare the faceprint from two separate images to try and determine whether they’re the same person. It can also try to guess other characteristics (like sex and emotion) about the individual from the faceprint. The most widely deployed class of face recognition is often called “face matching.” It tries to match two or more faceprints to determine if they are the same person.
In the case of facial biometrics, a 2D or 3D sensor “captures” a face. It then transforms it into digital data by applying an algorithm before comparing the image captured to those held in a database. These automated systems can be used to identify or check the identity of individuals in just a few seconds based on their facial features: spacing of the eyes, bridge of the nose, the contour of the lips, ears, chin, etc. They can even do this in the middle of a crowd and within dynamic and unstable environments. Proof of this can be seen in the performance achieved by Thales’ Live Face Identification System (LFIS), an advanced solution resulting from our long-standing expertise in biometrics, says Intel.
The technology was brought into prominence in 2014, when Facebook launched DeepFace, which was able to determine whether two pictures were of the same person with 97.25% accuracy (while humans scored 97.53%)or just 0.28% better than the Facebook program. Apple wasn’t far behind as it filed a patent in 2014 for AI technology that could analyze and identify mood based on facial expressions. In early 2016, they acquired Emotient, a facial analysis and emotion recognition software company.
Microsoft debuted their facial recognition software by Microsoft Project Oxford in 2015. Their Microsoft Face API has been used since then in Microsoft Hello, their equivalent to Apple’s Face ID, and they even offer a free demo of their emotion recognition technology online. Google also debuted their facial recognition technology called FaceNet in 2015, scoring 100% accuracy on a test to label faces in the wild, and has continued to add facial detection, recognition, and emotional content analysis to their Cloud Vision API services. Amazon’s computer vision division has Rekognition, which not only scans for facial recognition and emotion, but could be used to recognize 100 people in a single image and match them to a database of tens of millions. Snapchat filed a patent for emotion recognition AI in early 2018, likely using the treasure trove of face data gleaned from over 300 million active users per month trying on rainbow vomit and dog ear filters.
The very latest results of tests conducted in March 2018 and published in May by the US Homeland Security Science and Technology Directorate, known as the Biometric Technology Rally, also provide an excellent indication of the best face recognition software available on the market. The GaussianFace algorithm developed in 2014 by researchers at The Chinese University of Hong Kong achieved facial identification scores of 98.52% compared with the 97.53% achieved by humans. An excellent rating, despite weaknesses regarding memory capacity required and calculation times.
In June 2015, Google went one better with FaceNet. On the widely used Labeled Faces in the Wild (LFW) dataset, FaceNet achieved a new record accuracy of 99.63% (0.9963 ± 0.0009). Using an artificial neural network and a new algorithm, the company from Mountain View has managed to link a face to its owner with almost perfect results. This technology is incorporated into Google Photos and used to sort pictures and automatically tag them based on the people recognized. Proving its importance in the biometrics landscape, it was quickly followed by the online release of an unofficial open-source version known as OpenFace.
The technology is also been driven by availability of large facial data. Disney has access to at least 16 million faces of moviegoers, thanks to a collaboration with Caltech in processing emotional data in real time. Another leading player in the facial recognition industry is Affectiva, co-founded in 2009 by Rana el Kaliouby and Rosalind Picard while they studied at MIT’s Affective Computing lab, which has now analyzed over 7.5 million faces from 87 countries.
High-quality cameras in mobile devices have made facial recognition a viable option for authentication as well as identification. Apple’s iPhone X, for example, includes Face ID technology that lets users unlock their phones with a faceprint mapped by the phone’s camera. Businesses can analyze their customers’ faces to help tailor marketing strategies to people of different genders, ages and ethnic backgrounds. There are even consumer services that take advantage of facial recognition, like virtual eyeglass fitting and virtual makeovers.
The software identifies 80 nodal points on a human face. In this context, nodal points are endpoints used to measure variables of a person’s face, such as the length or width of the nose, the depth of the eye sockets and the shape of the cheekbones. The system works by capturing data for nodal points on a digital image of an individual’s face and storing the resulting data as a faceprint. The faceprint is then used as a basis for comparison with data captured from faces in an image or video.
Even though the facial recognition system only uses 80 nodal points, it can quickly and accurately identify target individuals when the conditions are favorable. However, if the subject’s face is partially obscured or in profile rather than facing forward, or if the light is insufficient, this type of software is less reliable. According to the National Institute of Standards and Technology (NIST), the incidence of false positives in facial recognition systems has been halved every two years since 1993.
Facebook uses facial recognition software to tag individuals in photographs. Each time an individual is tagged in a photograph, the software stores mapping information about that person’s facial characteristics. Once enough data has been collected, the software can use that information to identify a specific individual’s face when it appears in a new photograph. To protect people’s privacy, a feature called Photo Review notifies the Facebook member who has been identified.
A study done by MIT researchers in February 2018 found that Microsoft, IBM, and China-based Megvii (FACE++) tools had high error rates when identifying darker-skin women compared to lighter-skin men. At the end of June 2018, Microsoft announced in a blog post that it had made substantial improvements to its biased facial recognition technology. In May 2018, Ars Technica reported that Amazon is already actively promoting its cloud-based face recognition service named Rekognition to law enforcement agencies. The solution could recognize as many as 100 people in a single image and can perform face match against databases containing tens of millions of faces. In July, Newsweek reported that Amazon’s facial recognition technology falsely identified 28 members of US Congress as people arrested for crimes.
The feature common to all these disruptive technologies is known as Artificial Intelligence (AI) and, more precisely, deep learning where a system is capable of learning from data. It’s a central component of the latest-generation algorithms developed by Thales and other key players in the market. It holds the secret to face detection, face tracking, and face match as well as real-time translation of conversations. The result is that Face recognition systems are getting better all the time.
Face recognition CCTV systems can significantly accelerate operators’ efforts by enabling them to add a reference photo provided by the missing child’s parents and match it with past appearances of that face captured on video. Police can use face recognition to search video sequences (aka video analytics) of the estimated location and time the child has been declared missing. Police officers can better figure out the child’s movements before going missing and locate where he/she was last seen. A real-time alert can trigger an alarm whenever there’s a match. Police can then confirm its accuracy and do what’s necessary to recover the missing children. The same process can be applied for disoriented missing adults (e.g. with dementia, amnesia, epilepsy, or Alzheimer’s disease).
Isolating the appearances of specific individuals in a video sequence is critical. It can accelerate investigators’ jobs in child exploitation cases as well. Video analytics can help build chronologies, track activity on a map, reveal details and discover non-obvious connections among the players in a case.
Facial recognition has many challenges too. The prerequisites of an effective Facial recognition system are an exhaustive digital library or inventory of resident or terrorist facial data, an excellent camera for capturing the images of individuals approaching the camp, secure and fast communications and a strong processor for mapping and matching algorithms to produce results in real-time. Add to this the challenges of poor visibility conditions, changes in surroundings, quality of photographs and finally the learning algorithm.
If the intent is to ‘positively identify’, i.e., confirm the identity of a terrorist, militant or an over ground worker (OGW), an exhaustive database of such terrorists needs to be maintained. Such efforts may be hampered by the lack of updated photographs of such terrorists.
The great difficulty is ensuring that this process is carried out in real-time, something that is not available to all biometric face recognition software providers. Maintaining them in a CI/CT environment along with adequate power backup is a challenge, though these can still be taken care of.
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.
Facial recognition is never perfect, but it is alarmingly more error-prone when applied to anyone who is not a white and cisgender man. In a pioneering study from 2018, Joy Buolamwini and Dr. Timnit Gebru showed that face identification systems misidentified women of color at more than 40 times the rate of white men. More recently, NIST testing of various state-of-the-art face recognition systems confirmed a broad, dramatic trend of disparate “false positive” rates across demographics, with higher error rates for faces that were not white and male.
According to a recent NIST report, massive gains in accuracy have been made in the last five years (2013- 2018) and exceed improvements achieved in the 2010-2013 period. Most of the face recognition algorithms in 2018 outperform the most accurate algorithm from late 2013. In its 2018 test, NIST found that 0.2% of searches, in a database of 26.6 million photos, failed to match the correct image, compared with a 4% failure rate in 2014. It’s a 20x improvement over four years.
Scientists have given these systems a boost substantial enough to recognize individuals with partially covered faces. The identification accuracy reaches – and often exceeds – a whopping 90% in situations where only half a face is visible.
Facial recognition vulnerable to hacking
Facial recognition has also been fooled and shown vulnerable to hacking. A user could apply a filter that modifies specific pixels in an image before putting it on the web. These changes are imperceptible to the human eye but are very confusing for facial recognition algorithms.
In Russia, Grigory Bakunov has invented a solution to escape the eyes permanently watching our movements and confuse face detection devices. He has developed an algorithm that creates special makeup to fool the software. However, he has chosen not to bring his product to market after realizing how easily criminals could use it. In Germany, Berlin artist Adam Harvey has come up with a similar device known as CV Dazzle. He is now working on clothing featuring patterns to prevent detection. The hyperface camouflage includes patterns in fabric, such as eyes and mouths, to fool the face recognition system.
In late 2017, a Vietnamese company successfully used a mask to hack the Face ID face recognition function of Apple’s iPhone X. However, the hack is too complicated to implement for large-scale exploitation. Around the same time, researchers from a German company revealed a hack that allowed them to bypass the facial authentication of Windows 10 Hello by printing a facial image in infrared. Forbes announced in an article from May 2018 that researchers from the University of Toronto have developed an algorithm to disrupt facial recognition software (aka privacy filter). In August 2020, the Verge detailed a “cloaking” app named Fawkes. The software imperceptibly distorts your selfies and other pics you may leave on social media. The tool is coming from the University of Chicago’s Sand Lab.
The industry is working on anti-spoofing mechanisms, and two topics have been specifically identified by standardization groups : Make sure the captured image has been done from a person and not from a photograph (2D), a video screen (2D) or a mask (3D), (liveness check or liveness detection) Make sure that facial images (morphed portraits) of two or more individuals have not been joined into a reference document, such as a passport.
The identification and authentication solutions of the future will borrow from all aspects of biometrics. This will lead to “biometrix” or a biometric mix capable of guaranteeing total security and privacy for all stakeholders in the ecosystem. It’s very much the spirit of Thales Gemalto IdCloud Fraud Prevention, a risk assessment, and fraud detection software for payments. In this solution, geolocation, IP-addresses (the device being used) and keying patterns can create a strong combination to authenticate users for on-line banking or egovernment services securely.
The Facial Recognition market is projected to reach US$ 12,670.22 million by 2028 from US$ 5,012.71 million in 2021; it is expected to grow at a CAGR of 14.2% from 2021 to 2028.
The major factors driving the growth of the facial recognition market are the growing importance of the surveillance industry, increasing investment in facial recognition technologies by the government and defense sector, and increasing technological advancement across industry verticals. However, facial occlusion and face detection error, and lack of knowledge and awareness are some of the major challenges hindering the growth of the facial recognition market.
The use of facial recognition in law enforcement and non-law enforcement applications is predicted to increase rapidly during the forecast period. Furthermore, facial recognition is often preferred over other biometric technologies, such as voice recognition, skin texture recognition, iris identification, and fingerprint scanning, due to its contactless procedure and easy deployment.
The demand for facial recognition in monitoring and tracking of people’s movement, identity verification, security measures, patient identification, adoption of facial recognition technology by Q4 2021, will be a common phenomenon among various sectors across the globe. New use cases such as biometric sign-in, public security, travel security, authorized healthcare services, eLearning platforms, and many other facial recognition systems are expected to be deployed at a large scale. These kinds of contactless verification technologies have become of prior importance amid the pandemic situation. To avoid the social distancing, scanners that involve touch and transfer bacteria and viruses, such as fingerprint scanners, are expected to lose demand in the future, which will create demand for contactless technologies, such as facial recognition, by 2021
Technology trends such as automation, artificial intelligence, and data analytics are aiding the development of robust solutions on public safety. System integration technologies will also play an instrumental role in development of advanced public safety solutions. These technologies will be responsible in bringing considerable efficiency in information sharing, value assessment and redundancy lowering operations of a national counterterrorism system.
Tech giants such as International Business Machines Corp., popularly known as IBM, are also actively participating in the global counter terror and public safety technologies market. Other leading companies in the global counter terror and public safety technology market include, AT&T Inc., Accenture PLC, ABB Ltd., 3xLOGIC, Inc., AeroVironment, Inc., ACTi Corporation, Avigilon Corporation, The ADT Corporation, Alcatel-Lucent France, S.A., and Airbus SE.
The growth of the global facial recognition market is expected to be driven by various factors, such as the increasing need for enhanced surveillance and monitoring at public places and the increasing use of facial recognition technologies in industries, such as the government.
The 3D facial recognition technology is independent of illumination, which enables it to capture high-quality images in uncontrolled environments, such as poorly lit and or completely dark areas. The 3D facial recognition model overcomes the drawbacks of the 2D facial recognition technology. The 3D facial recognition technologies have a high potential to analyze, identify, and verify the facial characteristics of individuals. The technologies are also used in application areas, such as cross-border monitoring, document verification, and identity management.
Asia Pacific (APAC) is expected to grow at the highest CAGR during the forecast period. Factors such as huge investments from the government sector toward security and surveillance infrastructure, increased public awareness, and the emergence of sophisticated technologies backed by analytics are said to be driving the facial recognition market growth in APAC significantly.
The facial recognition market includes various facial recognition vendors, such as Aware (US), NEC Corporation (Japan), Ayonix Corp. (Japan), Cognitec Systems (Germany), KeyLemon (Switzerland), nViso (Switzerland), Herta Security (Spain), Techno Brain (Kenya), Neurotechnology (Lithuania), Daon (US), Animetrics (US), 3M Company (US), IDEMIA (France), and Gemalto (Netherlands).
Facial recognition software
Apple’s iPhone X’s software, which is designed with 3-D modeling to resist being spoofed by photos or masks, captures and compares over 30,000 variables. Similar to the fingerprint-based Touch ID found in previous Apple devices, the use of Face ID also allows users to access Apple Pay, the App Store, iTunes and some third-party apps.
Developers can use Amazon Rekognition, an image analysis service that’s part of the Amazon AI suite, to add facial recognition and analysis features to an application. Google provides a similar capability with its Google Cloud Vision API. The technology, which uses machine learning to detect, match and identify faces, is being used in a wide variety of ways, including entertainment and marketing. The Kinect motion gaming system, for example, uses facial recognition to differentiate among players. Smart advertisements in airports are now able to identify the gender, ethnicity and approximate age of a passersby and target the advertisement to the person’s demographic.
PimEyes is a facial recognition search engine. If you upload a picture of your face to PimEyes’ website, it will immediately show you any pictures of yourself that the company has found on the internet. PimEyes is open to anyone with internet access. It’s a stark contrast from Clearview AI, which became well-known for building its enormous stash of faces with images of people from social networks and limits its use to law enforcement (Clearview has said it has hundreds of such customers). “Using the latest technologies, artificial intelligence and machine learning, we help you find your pictures on the Internet and defend yourself from scammers, identity thieves, or people who use your image illegally,” the website declares.
Beijing-based Megvii Technology’s Face++ offers a menu of facial analysis services including face search, eye tracking, emotion recognition, skin health analysis, skeleton detection, beauty score, and many, many others. Now China has added Emotional surveillance to its wide surveillance network of facial recognition and internet censorship across China. South China Morning Post reported that Employees’ brain activity and emotions are reportedly being monitored in factories, state-owned enterprises, and the military across China.
Moreover, two companies in China have developed AI-based coronavirus diagnostic software to detect lung problems using CT scans. At least 34 Chinese hospitals used this technology to screen 32,000 suspected cases in February 2020. Hence, the overall impact of COVID-19 pandemic on the market is low to moderate.
Tech5, IDEMIA, Aware Inc., Cognitec Systems GmbH, Ayonix Corporation, Fujitsu Limited, Onfido, NEC Corporation, Thales Group, and Face PHI are among the key players operating in the global facial recognition market and profiled in the market study.
“Biometric” identification systems
Facial recognition can also serve as one of several methods of “biometric” identification systems. Biometrics are used to identify and authenticate a person using a set of recognizable and verifiable data unique and specific to that person. Facial recognition is a category of biometric software that maps an individual’s facial features mathematically and stores the data as a faceprint. The software uses deep learning algorithms to compare a live capture or digital image to the stored faceprint in order to verify an individual’s identity.
These systems examine physical features of a person’s body in an attempt to uniquely distinguish one person from all the others. The UIDAI or Unique Identification Authority of India has allowed face recognition as an additional means of Aadhaar authentication. The new method, called face authentication, will be used in combination with existing ways such as fingerprint or iris scan, the UIDAI said. The move is aimed at providing easy authentication for those individuals who face a difficulty in other biometric authentication like fingerprint and iris, the UIDAI said.
In India, the Aadhaar project is the largest biometric database in the world. It already provides a unique digital identity number to 1.26 billion residents as of August 2020. Face authentication will be available as an add-on service in fusion mode along with one more authentication factor like fingerprint, Iris, or OTP. India could also roll-out the world’s most extensive face recognition system in 2020. The National Crime Records Bureau (NCRB) has issued an RFP inviting bids to develop a nationwide facial recognition system. According to the 160-page document, the system will be a centralized web application hosted at the NCRB Data Center in Delhi. It will be available for access to all the police stations.
In Brazil, the Superior Electoral Court (Tribunal Superior Eleitoral) is involved in a nationwide biometric data collection project. The aim is to create a biometric database and unique ID cards by 2020, recording the information of 140 million citizens. Russia’s Central Bank has been deploying a countrywide program since 2017 designed to collect faces, voices, iris scans, and fingerprints. But the process is progressing very slowly according to the Biometricupdate website of 13 March 2019. The city of Moscow claims one of the world’s largest network of 160,000 surveillance cameras by the end of 2019 and are to be fitted with facial recognition technology for public safety.
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