Facial recognition system
Automatic ticket gate with face recognition system in Osaka Metro Morinomiya Station
While initially a form of computer application
, facial recognition systems have seen wider uses in recent times on smartphones
and in other forms of technology, such as robotics
. Because computerized facial recognition involves the measurement of a human's physiological characteristics facial recognition systems are categorised as biometrics
. Although the accuracy of facial recognition systems as a biometric technology is lower than iris recognition
and fingerprint recognition
, it is widely adopted due to its contactless process.
Facial recognition systems have been deployed in advanced human-computer interaction
, video surveillance
and automatic indexing
They are also used widely by law enforcement agencies.
History of facial recognition technology
Automated facial recognition was pioneered in the 1960s. Woody Bledsoe
, Helen Chan Wolf
, and Charles Bisson worked on using the computer to recognize human faces. Their early facial recognition project was dubbed "man-machine" because the coordinates of the facial features in a photograph had to be established by a human before they could be used by the computer for recognition. On a graphics tablet
a human had to pinpoint the coordinates of facial features such as the pupil centers, the inside and outside corner of eyes, and the widows peak
in the hairline. The coordinates were used to calculate 20 distances, including the width of the mouth and of the eyes. A human could process about 40 pictures an hour in this manner and so build a database of the computed distances. A computer would then automatically compare the distances for each photograph, calculate the difference between the distances and return the closed records as a possible match.
In 1970, Takeo Kanade
publicly demonstrated a face matching system that located anatomical features such as the chin and calculated the distance ratio between facial features without human intervention. Later tests revealed that the system could not always reliably identify facial features. Nonetheless, interest in the subject grew and in 1977 Kanade published the first detailed book on facial recognition technology.
In 1993, the Defense Advanced Research Project Agency
(DARPA) and the Army Research Laboratory
(ARL) established the face recognition technology program FERET
to develop "automatic face recognition capabilities" that could be employed in a productive real life environment "to assist security, intelligence, and law enforcement personnel in the performance of their duties." Face recognition systems that had been trialed in research labs were evaluated and the FERET tests found that while the performance of existing automated facial recognition systems varied, a handful of existing methods could viably be used to recognize faces in still images taken in a controlled environment.
The FERET tests spawned three US companies that sold automated facial recognition systems. Vision Corporation and Miros Inc were both founded in 1994, by researchers who used the results of the FERET tests as a selling point. Viisage Technology
was established by a identification card
defense contractor in 1996 to commercially exploit the rights to the facial recognition algorithm developed by Alex Pentland
Following the 1993 FERET face recognition vendor test
the Department of Motor Vehicles
(DMV) offices in West Virginia
and New Mexico
were the first DMV offices to use automated facial recognition systems as a way to prevent and detect people obtaining multiple driving licenses
under different names. Driver's licenses in the United States
were at that point a commonly accepted from of photo identification
. DMV offices across the United States were undergoing a technological upgrade and were in the process of establishing databases of digital ID photographs. This enabled DMV offices to deploy the facial recognition systems on the market to search photographs for new driving licenses against the existing DMV database.
DMV offices became one of the first major markets for automated facial recognition technology and introduced US citizens to facial recognition as a standard method of identification.
The increase of the US prison population
in the 1990s prompted U.S. states
to established connected and automated identification systems that incorporated digital biometric
databases, in some instances this included facial recognition. In 1999 Minnesota
incorporated the facial recognition system FaceIT by Visionics into a mug shot
booking system that allowed police, judges and court officers to track criminals across the state.
In this shear mapping
the red arrow changes direction, but the blue arrow does not and is used as eigenvector.
The Viola–Jones algorithm for face detection uses Haar-like features
to locate faces in an image. Here a Haar Feature that looks similar to the bridge of the nose is applied onto the face.
Until the 1990s facial recognition systems were developed primarily by using photographic portraits
of human faces. Research on face recognition to reliably locate a face in an image that contains other objects gained traction in the early 1990s with the principle component analysis
(PCA). The PCA method of face detection is also known as Eigenface
and was developed by Matthew Turk and Alex Pentland.
Turk and Pentland combined the conceptual approach of the Karhunen–Loève theorem
and factor analysis
, to develop a linear model
. Eigenfaces are determined based on global and orthogonal
features in human faces. A human face is calculated as a weighted
combination of a number of Eigenfaces. Because few Eigenfaces were used to encode human faces of a given population, Turk and Pentland's PCA face detection method greatly reduced the amount of data that had to be processed to detect a face. Pentland in 1994 defined Eigenface features, including eigen eyes, eigen mouths and eigen noses, to advance the use of PCA in facial recognition. In 1997 the PCA Eigenface method of face recognition
was improved upon using linear discriminant analysis
(LDA) to produce Fisherfaces
LDA Fisherfaces became dominantly used in PCA feature based face recognition. While Eigenfaces were also used for face reconstruction. In these approaches no global structure of the face is calculated which links the facial features or parts.
Purely feature based approaches to facial recognition were overtaken in the late 1990s by the Bochum system, which used Gabor filter
to record the face features and computed a grid
of the face structure to link the features.Christoph von der Malsburg
and his research team at the University of Bochum
developed Elastic Bunch Graph Matching
in the mid 1990s to extract a face out of an image using skin segmentation.
By 1997 the face detection method developed by Malsburg outperformed most other facial detection systems on the market. The so-called "Bochum system" of face detection was sold commercially on the market as ZN-Face
to operators of airports
and other busy locations. The software was "robust enough to make identifications from less-than-perfect face views. It can also often see through such impediments to identification as mustaches, beards, changed hairstyles and glasses—even sunglasses".
Techniques for face recognition
Automatic face detection with OpenCV
can recognize faces without much effort,
facial recognition is a challenging pattern recognition
problem in computing
. Facial recognition systems attempt to identify a human face, which is three-dimensional and changes in appearance with lighting and facial expression, based on its two-dimensional image. To accomplish this computational task, facial recognition systems perform four steps. First face detection
is used to segment the face from the image background. In the second step the segmented face image is aligned to account for face pose
, image size and photographic properties, such as illumination
. The purpose of the alignment process is to enable the accurate localization of facial features in the third step, the facial feature extraction. Features such as eyes, nose and mouth are pinpointed and measured in the image to represent the face. The so established feature vector
of the face is then, in the fourth step, matched against a database of faces.
Some face recognition algorithms
identify facial features by extracting landmarks, or features, from an image of the subject's face. For example, an algorithm may analyze the relative position, size, and/or shape of the eyes, nose, cheekbones, and jaw.
These features are then used to search for other images with matching features.
Other algorithms normalize
a gallery of face images and then compress the face data, only saving the data in the image that is useful for face recognition. A probe image is then compared with the face data.
One of the earliest successful systems
is based on template matching techniques
applied to a set of salient facial features, providing a sort of compressed face representation.
Recognition algorithms can be divided into two main approaches: geometric, which looks at distinguishing features, or photo-metric, which is a statistical approach that distills an image into values and compares the values with templates to eliminate variances. Some classify these algorithms into two broad categories: holistic and feature-based models. The former attempts to recognize the face in its entirety while the feature-based subdivide into components such as according to features and analyze each as well as its spatial location with respect to other features.
Human identification at a distance (HID)
To enable human identification at a distance (HID) low-resolution images of faces are enhanced using face hallucination
. In CCTV
imagery faces are often very small. But because facial recognition algorithms that identify and plot facial features require high resolution images, resolution enhancement techniques have been developed to enable facial recognition systems to work with imagery that has been captured in environments with a high signal-to-noise ratio
. Face hallucination algorithms that are applied to images prior to those images being submitted to the facial recognition system utilise example-based machine learning with pixel substitution or nearest neighbour distribution
indexes that may also incorporate demographic and age related facial characteristics. Use of face hallucination techniques improves the performance of high resolution facial recognition algorithms and may be used to overcome the inherent limitations of super-resolution algorithms. Face hallucination techniques are also used to pre-treat imagery where faces are disguised. Here the disguise, such as sunglasses, is removed and the face hallucination algorithm is applied to the image. Such face hallucination algorithms need to be trained on similar face images with and without disguise. To fill in the area uncovered by removing the disguise, face hallucination algorithms need to correctly map the entire state of the face, which may be not possible due to the momentary facial expression captured in the low resolution image.
3D model of a human face.
Three-dimensional face recognition
technique uses 3D sensors to capture information about the shape of a face. This information is then used to identify distinctive features on the surface of a face, such as the contour of the eye sockets, nose, and chin.
One advantage of 3D face recognition is that it is not affected by changes in lighting like other techniques. It can also identify a face from a range of viewing angles, including a profile view.
Three-dimensional data points from a face vastly improve the precision of face recognition. 3D-dimensional face recognition research is enabled by the development of sophisticated sensors that project structured light onto the face.
3D matching technique are sensitive to expressions, therefore researchers at Technion
applied tools from metric geometry
to treat expressions as isometries
A new method of capturing 3D images of faces uses three tracking cameras that point at different angles; one camera will be pointing at the front of the subject, second one to the side, and third one at an angle. All these cameras will work together so it can track a subject's face in real-time and be able to face detect and recognize.
image of two people taken in long-wavelength infrared (body-temperature thermal) light.
A different form of taking input data for face recognition is by using thermal cameras
, by this procedure the cameras will only detect the shape of the head and it will ignore the subject accessories such as glasses, hats, or makeup.
Unlike conventional cameras, thermal cameras can capture facial imagery even in low-light and nighttime conditions without using a flash and exposing the position of the camera.
However, the databases for face recognition are limited. Efforts to build databases of thermal face images date back to 2004.
By 2016 several databases existed, including the IIITD-PSE and the Notre Dame thermal face database.
Current thermal face recognition systems are not able to reliably detect a face in a thermal image that has been taken of an outdoor environment.
In 2018, researchers from the U.S. Army Research Laboratory (ARL)
developed a technique that would allow them to match facial imagery obtained using a thermal camera with those in databases that were captured using a conventional camera.
Known as a cross-spectrum synthesis method due to how it bridges facial recognition from two different imaging modalities, this method synthesize a single image by analyzing multiple facial regions and details.
It consists of a non-linear regression model that maps a specific thermal image into a corresponding visible facial image and an optimization issue that projects the latent projection back into the image space.
ARL scientists have noted that the approach works by combining global information (i.e. features across the entire face) with local information (i.e. features regarding the eyes, nose, and mouth).
According to performance tests conducted at ARL, the multi-region cross-spectrum synthesis model demonstrated a performance improvement of about 30% over baseline methods and about 5% over state-of-the-art methods.
Founded in 2013, Looksery
went on to raise money for its face modification app on Kickstarter. After successful crowdfunding, Looksery
launched in October 2014. The application allows video chat with others through a special filter for faces that modifies the look of users. Image augmenting
applications already on the market, such as FaceTune
and Perfect365, were limited to static images, whereas Looksery allowed augmented reality to live videos. In late 2015 SnapChat
purchased Looksery, which would then become its landmark lenses function.
Snapchat filter applications use face detection technology and on the basis of the facial features identified in an image a 3D mesh mask is layered over the face.
's algorithm has been regarded as especially effective, but many were left to wonder at the exact programming that caused the app to be so effective in guessing the user's desired content.
In June 2020, Tiktok released a statement regarding the "For You" page, and how they recommended videos to users, which did not include facial recognition.
In February 2021, however, Tiktok agreed to a $92 million settlement to a US lawsuit which alleged that the app had used facial recognition in both user videos and its algorithm to identify age, gender and ethnicity.
The emerging use of facial recognition is in the use of ID verification services
. Many companies and others are working in the market now to provide these services to banks, ICOs, and other e-businesses.
Face recognition has been leveraged as a form of biometric authentication
for various computing platforms and devices;Android 4.0 "Ice Cream Sandwich"
added facial recognition using a smartphone
's front camera as a means of unlocking
introduced face recognition login to its Xbox 360
video game console through its Kinect
as well as Windows 10
via its "Windows Hello" platform (which requires an infrared-illuminated camera).
In 2017 Apple's iPhone X
smartphone introduced facial recognition to the product line with its "Face ID
" platform, which uses an infrared illumination system.
introduced Face ID
on the flagship iPhone X as a biometric authentication successor to the Touch ID
, a fingerprint
based system. Face ID has a facial recognition sensor that consists of two parts: a "Romeo" module that projects more than 30,000 infrared dots onto the user's face, and a "Juliet" module that reads the pattern.
The pattern is sent to a local "Secure Enclave" in the device's central processing unit
(CPU) to confirm a match with the phone owner's face.
The facial pattern is not accessible by Apple. The system will not work with eyes closed, in an effort to prevent unauthorized access.
The technology learns from changes in a user's appearance, and therefore works with hats, scarves, glasses, and many sunglasses, beard and makeup.
It also works in the dark. This is done by using a "Flood Illuminator", which is a dedicated infrared
flash that throws out invisible infrared light onto the user's face to properly read the 30,000 facial points.
Deployment in security services
Police forces in the United Kingdom
have been trialing live facial recognition technology at public events since 2015.
In May 2017, a man was arrested using an automatic facial recognition (AFR) system mounted on a van operated by the South Wales Police. Ars Technica
reported that "this appears to be the first time [AFR] has led to an arrest".
However, a 2018 report by Big Brother Watch
found that these systems were up to 98% inaccurate.
The report also revealed that two UK
police forces, South Wales Police
and the Metropolitan Police
, were using live facial recognition at public events and in public spaces.
In September 2019, South Wales Police use of facial recognition was ruled lawful.
Live facial recognition has been trialled since 2016 in the streets of London
and will be used on a regular basis from Metropolitan Police
from beginning of 2020.
In August 2020 the Court of Appeal
ruled that the way the facial recognition system had been used by the South Wales Police in 2017 and 2018 violated human rights.
The U.S. Department of State
operates one of the largest face recognition systems in the world with a database of 117 million American adults, with photos typically drawn from driver's license photos.
Although it is still far from completion, it is being put to use in certain cities to give clues as to who was in the photo. The FBI uses the photos as an investigative tool, not for positive identification.
As of 2016, facial recognition was being used to identify people in photos taken by police in San Diego
and Los Angeles
(not on real-time video, and only against booking photos)
and use was planned in West Virginia
In recent years Maryland has used face recognition by comparing people's faces to their driver's license photos. The system drew controversy when it was used in Baltimore to arrest unruly protesters after the death of Freddie Gray
in police custody.
Many other states are using or developing a similar system however some states have laws prohibiting its use.
Starting in 2018, U.S. Customs and Border Protection
deployed "biometric face scanners" at U.S. airports. Passengers taking outbound international flights can complete the check-in, security and the boarding process after getting facial images captured and verified by matching their ID photos stored on CBP's database. Images captured for travelers with U.S. citizenship will be deleted within up to 12-hours. TSA
had expressed its intention to adopt a similar program for domestic air travel during the security check process in the future. The American Civil Liberties Union
is one of the organizations against the program, concerning that the program will be used for surveillance purposes.
In 2019, researchers reported that Immigration and Customs Enforcement
uses facial recognition software against state driver's license databases, including for some states that provide licenses to undocumented immigrants.
In 2006, the Skynet Project was initiated by the Chinese Government to implement CCTV surveillance nationwide and as of 2018, there has been 20 million cameras, many of which capable of real-time facial recognition, deployed across the country for this project
Some official claim that the current Skynet system can scan the entire Chinese population in one second and the world population in two seconds.
In 2017 the Qingdao
police was able to identify twenty-five wanted suspects using facial recognition equipment at the Qingdao International Beer Festival, one of which had been on the run for 10 years.
The equipment works by recording a 15-second video clip and taking multiple snapshots of the subject. That data is compared and analyzed with images from the police department's database and within 20 minutes, the subject can be identified with a 98.1% accuracy.
In 2018, Chinese police in Zhengzhou
were using smart glasses to take photos which are compared against a government database using facial recognition to identify suspects, retrieve an address, and track people moving beyond their home areas.
As of late 2017, China has deployed facial recognition and artificial intelligence
technology in Xinjiang
. Reporters visiting the region found surveillance cameras installed every hundred meters or so in several cities, as well as facial recognition checkpoints at areas like gas stations, shopping centers, and mosque entrances.
In May 2019, Human Rights Watch
reported finding Face++ code in the Integrated Joint Operations Platform
(IJOP), a police surveillance app used to collect data on, and track the Uighur
community in Xinjiang
Human Rights Watch released a correction to its report in June 2019 stating that the Chinese company Megvii
did not appear to have collaborated on IJOP, and that the Face++ code in the app was inoperable.
In February 2020, following the Coronavirus outbreak
, Megvii applied for a bank loan to optimize the body temperature screening system it had launched to help identify people with symptoms of a Coronavirus
infection in crowds. In the loan application Megvii stated that it needed to improve the accuracy of identifying masked individuals.
Many public places in China are implemented with facial recognition equipment, including railway stations, airports, tourist attractions, expos, and office buildings. In October 2019, a professor at Zhejiang Sci-Tech University
sued the Hangzhou Safari Park
for abusing private biometric information of customers. The safari park uses facial recognition technology to verify the identities of its Year Card holders. An estimated 300 tourist sites in China have installed facial recognition systems and use them to admit visitors. This case is reported to be the first on the use of facial recognition systems in China.
In August 2020 Radio Free Asia
reported that in 2019 Geng Guanjun, a citizen of Taiyuan City
who had used the WeChat
app by Tencent
to forward a video to a friend in the United States was subsequently convicted on the charge of the crime “picking quarrels and provoking troubles”. The Court documents showed that the Chinese police used a facial recognition system to identify Geng Guanjun as an "overseas democracy activist" and that China's network management and propaganda departments directly monitor WeChat users.
In 2019, Protestors in Hong Kong
destroyed smart lampposts amid concerns they could contain cameras and facial recognition system used for surveillance by Chinese authorities.
In the 2000 Mexican presidential election
, the Mexican government employed face recognition software to prevent voter fraud
. Some individuals had been registering to vote under several different names, in an attempt to place multiple votes. By comparing new face images to those already in the voter database, authorities were able to reduce duplicate registrations.
Police forces in at least 21 countries of the European Union use, or plan to use, facial recognition systems, either for administrative or criminal purposes.
Greek police passed a contract with Intracom-Telecom for the provision of at least 1,000 devices equipped with live facial recognition system. The delivery is expected before the summer 2021. The total value of the contract is over 4 million euros, paid for in large part by the Internal Security Fund of the European Commission
Italian police acquired a face recognition system in 2017, Sistema Automatico Riconoscimento Immagini (SARI). In November 2020, the Interior ministry announced plans to use it in real-time to identify people suspected of seeking asylum.
has deployed facial recognition and artificial intelligence technology since 2016.
The database of the Dutch police currently contains over 2.2 million pictures of 1.3 million Dutch citizens. This accounts for about 8% of the population. Hundreds of cameras have been deployed in the city of Amsterdam alone.
In South Africa, in 2016, the city of Johannesburg announced it was rolling out smart CCTV cameras complete with automatic number plate recognition and facial recognition.
Deployment in retail stores
The US firm 3VR, now Identiv
, is an example of a vendor
which began offering facial recognition systems and services to retailers
as early as 2007.
In 2012 the company advertised benefits such as "dwell and queue line analytics to decrease customer wait times", "facial surveillance analytic[s] to facilitate personalized customer greetings by employees
" and the ability to "[c]reate loyalty programs by combining point of sale (POS)
data with facial recognition".
In July 2020, the Reuters news agency
reported that during the 2010s the pharmacy
chain Rite Aid
had deployed facial recognition video surveillance
systems and components from FaceFirst, DeepCam LLC, and other vendors at some retail locations in the United States.
Cathy Langley, Rite Aid's vice president of asset protection, used the phrase "feature matching" to refer to the systems and said that usage of the systems resulted in less violence and organized crime in the company's stores, while former vice president of asset protection Bob Oberosler emphasized improved safety for staff and a reduced need for the involvement of law enforcement organizations
In a 2020 statement to Reuters in response to the reporting, Rite Aid said that it had ceased using the facial recognition software and switched off the cameras.
According to director Read Hayes
of the National Retail Federation Loss Prevention Research Council, Rite Aid's surveillance program was either the largest or one of the largest programs in retail. The Home Depot
, and 7-Eleven
are among other US retailers also engaged in large-scale pilot programs
or deployments of facial recognition technology.
Face recognition systems have also been used by photo management software to identify the subjects of photographs, enabling features such as searching images by person, as well as suggesting photos to be shared with a specific contact if their presence were detected in a photo.
By 2008 facial recognition systems were typically used as access control in security systems
On August 18, 2019, The Times
reported that the UAE-owned Manchester City
hired a Texas-based firm, Blink Identity, to deploy facial recognition systems in a driver program. The club has planned a single super-fast lane for the supporters at the Etihad stadium
However, civil rights groups cautioned the club against the introduction of this technology, saying that it would risk "normalising a mass surveillance tool". The policy and campaigns officer at Liberty
, Hannah Couchman said that Man City's move is alarming, since the fans will be obliged to share deeply sensitive personal information with a private company, where they could be tracked and monitored in their everyday lives.
In August 2020, amid the COVID-19 pandemic in the United States
, American football stadiums of New York and Los Angeles announced the installation of facial recognition for upcoming matches. The purpose is to make the entry process as touchless as possible.
Disney's Magic Kingdom
, near Orlando, Florida
, likewise announced a test of facial recognition technology to create a touchless experience during the pandemic; the test was originally slated to take place between March 23 and April 23, 2021 but the limited timeframe had been removed as of late April.
Advantages and disadvantages
Compared to other biometric systems
In 2006, the performance of the latest face recognition algorithms was evaluated in the Face Recognition Grand Challenge (FRGC)
. High-resolution face images, 3-D face scans, and iris images were used in the tests. The results indicated that the new algorithms are 10 times more accurate than the face recognition algorithms of 2002 and 100 times more accurate than those of 1995. Some of the algorithms were able to outperform human participants in recognizing faces and could uniquely identify identical twins.
One key advantage of a facial recognition system that it is able to perform mass identification as it does not require the cooperation of the test subject to work. Properly designed systems installed in airports, multiplexes, and other public places can identify individuals among the crowd, without passers-by even being aware of the system.
However, as compared to other biometric techniques, face recognition may not be most reliable and efficient. Quality measures are very important in facial recognition systems as large degrees of variations are possible in face images. Factors such as illumination, expression, pose and noise during face capture can affect the performance of facial recognition systems.
Among all biometric systems, facial recognition has the highest false acceptance and rejection rates,
thus questions have been raised on the effectiveness of face recognition software in cases of railway and airport security.
Ralph Gross, a researcher at the Carnegie Mellon Robotics Institute
in 2008, describes one obstacle related to the viewing angle of the face: "Face recognition has been getting pretty good at full frontal faces and 20 degrees off, but as soon as you go towards profile, there've been problems."
Besides the pose variations, low-resolution face images are also very hard to recognize. This is one of the main obstacles of face recognition in surveillance systems.
Face recognition is less effective if facial expressions
vary. A big smile can render the system less effective. For instance: Canada, in 2009, allowed only neutral facial expressions in passport photos.
There is also inconstancy in the datasets used by researchers. Researchers may use anywhere from several subjects to scores of subjects and a few hundred images to thousands of images. It is important for researchers to make available the datasets they used to each other, or have at least a standard dataset.
Facial recognition systems have been criticized for upholding and judging based on a binary gender
When classifying the faces of cisgender
individuals into male or female, these systems are often very accurate,
however were typically confused or unable to determine the gender identity
people. Gender norms
are being upheld by these systems, so much so that even when shown a photo of a cisgender male with long hair, algorithms was split between following the gender norm of males having short hair, and the masculine
facial features and became confused.
This accidental misgendering
of people can be very harmful for those who do not identify with their sex assigned at birth,
by disregarding and invalidating their gender identity. This is also harmful for people who do not ascribe to traditional and outdated gender norms, because it invalidates their gender expression
, regardless of their gender identity.
Critics of the technology complain that the London Borough of Newham
scheme has, as of 2004, never recognized a single criminal, despite several criminals in the system's database living in the Borough and the system has been running for several years. "Not once, as far as the police know, has Newham's automatic face recognition system spotted a live target."
This information seems to conflict with claims that the system was credited with a 34% reduction in crime (hence why it was rolled out to Birmingham also).
An experiment in 2002 by the local police
department in Tampa
, had similarly disappointing results.
A system at Boston's Logan Airport
was shut down in 2003 after failing to make any matches during a two-year test period.
In 2014, Facebook stated that in a standardized two-option facial recognition test, its online system scored 97.25% accuracy, compared to the human benchmark of 97.5%.
Systems are often advertised as having accuracy near 100%; this is misleading as the studies often use much smaller sample sizes than would be necessary for large scale applications. Because facial recognition is not completely accurate, it creates a list of potential matches. A human operator must then look through these potential matches and studies show the operators pick the correct match out of the list only about half the time. This causes the issue of targeting the wrong suspect.
Civil rights organizations and privacy campaigners such as the Electronic Frontier Foundation
, Big Brother Watch
and the ACLU
express concern that privacy
is being compromised by the use of surveillance technologies
Face recognition can be used not just to identify an individual, but also to unearth other personal data
associated with an individual – such as other photos featuring the individual, blog posts, social media profiles, Internet behavior, and travel patterns.
Concerns have been raised over who would have access to the knowledge of one's whereabouts and people with them at any given time.
Moreover, individuals have limited ability to avoid or thwart face recognition tracking unless they hide their faces. This fundamentally changes the dynamic of day-to-day privacy by enabling any marketer, government agency, or random stranger to secretly collect the identities and associated personal information of any individual captured by the face recognition system.Consumers
may not understand or be aware of what their data is being used for, which denies them the ability to consent to how their personal information gets shared.
In July 2015, the United States Government Accountability Office
conducted a Report to the Ranking Member, Subcommittee on Privacy, Technology and the Law, Committee on the Judiciary, U.S. Senate. The report discussed facial recognition technology's commercial uses, privacy issues, and the applicable federal law. It states that previously, issues concerning facial recognition technology were discussed and represent the need for updating the privacy laws of the United States
so that federal law continually matches the impact of advanced technologies. The report noted that some industry, government, and private organizations were in the process of developing, or have developed, "voluntary privacy guidelines". These guidelines varied between the stakeholders
, but their overall aim was to gain consent and inform citizens of the intended use of facial recognition technology. According to the report the voluntary privacy guidelines helped to counteract the privacy concerns that arise when citizens are unaware of how their personal data gets put to use.
In 2016 Russian company NtechLab caused a privacy scandal in the international media when it launched the FindFace
face recognition system with the promise that Russian users could take photos of strangers in the street and link them to a social media profile on the social media platform Vkontakte
In December 2017, Facebook rolled out a new feature that notifies a user when someone uploads a photo that includes what Facebook thinks is their face, even if they are not tagged. Facebook has attempted to frame the new functionality in a positive light, amidst prior backlashes.
Facebook's head of privacy, Rob Sherman, addressed this new feature as one that gives people more control over their photos online. “We’ve thought about this as a really empowering feature,” he says. “There may be photos that exist that you don’t know about.”
has become the subject of several class action lawsuits under the Biometric Information Privacy Act, with claims alleging that Facebook is collecting and storing face recognition data of its users without obtaining informed consent, in direct violation of the 2008 Biometric Information Privacy Act
The most recent case was dismissed in January 2016 because the court lacked jurisdiction.
In the US, surveillance companies such as Clearview AI
are relying on the First Amendment to the United States Constitution
to data scrape user accounts
on social media platforms for data that can be used in the development of facial recognition systems.
In 2019 the Financial Times
first reported that facial recognition software was in use in the King's Cross
area of London.
The development around London's King's Cross mainline station includes shops, offices, Google's UK HQ and part of St Martin's College. According to the UK Information Commissioner's Office
: "Scanning people's faces as they lawfully go about their daily lives, in order to identify them, is a potential threat to privacy that should concern us all."
The UK Information Commissioner Elizabeth Denham
launched an investigation into the use of the King's Cross facial recognition system, operated by the company Argent. In September 2019 it was announced by Argent that facial recognition software would no longer be used at King's Cross
. Argent claimed that the software had been deployed between May 2016 and March 2018 on two cameras covering a pedestrian street running through the centre of the development.
In October 2019 a report by the deputy London mayor Sophie Linden
revealed that in a secret deal the Metropolitan Police
had passed photos of seven people to Argent for use in their King's cross facial recognition system.
Imperfect technology in law enforcement
It is still contested as to whether or not facial recognition technology works less accurately on people of color.
One study by Joy Buolamwini
(MIT Media Lab) and Timnit Gebru
(Microsoft Research) found that the error rate for gender recognition for women of color within three commercial facial recognition systems ranged from 23.8% to 36%, whereas for lighter-skinned men it was between 0.0 and 1.6%. Overall accuracy rates for identifying men (91.9%) were higher than for women (79.4%), and none of the systems accommodated a non-binary understanding of gender.
It also showed that the datasets used to train commercial facial recognition models were unrepresentative of the broader population and skewed toward lighter-skinned males. However, another study showed that several commercial facial recognition software sold to law enforcement offices around the country had a lower false non-match rate for black people than for white people.
Experts fear that face recognition systems may actually be hurting citizens the police claims they are trying to protect.
It is considered an imperfect biometric, and in a study conducted by Georgetown University researcher Clare Garvie, she concluded that "there’s no consensus in the scientific community that it provides a positive identification of somebody.”
It is believed that with such large margins of error in this technology, both legal advocates and facial recognition software companies say that the technology should only supply a portion of the case – no evidence that can lead to an arrest of an individual.
The lack of regulations holding facial recognition technology companies to requirements of racially biased testing can be a significant flaw in the adoption of use in law enforcement. CyberExtruder
, a company that markets itself to law enforcement said that they had not performed testing or research on bias in their software. CyberExtruder did note that some skin colors are more difficult for the software to recognize with current limitations of the technology. “Just as individuals with very dark skin are hard to identify with high significance via facial recognition, individuals with very pale skin are the same,” said Blake Senftner, a senior software engineer at CyberExtruder.
In 2010 Peru
passed the Law for Personal Data Protection, which defines biometric information that can be used to identify an individual as sensitive data. In 2012 Colombia
passed a comprehensive Data Protection Law which defines biometric data as senstivite information.
According to Article 9(1) of the EU's 2016 General Data Protection Regulation
(GDPR) the processing of biometric data
for the purpose of "uniquely identifying a natural person" is sensitive and the facial recognition data processed in this way becomes sensitive personal data. In response to the GDPR passing into the law of EU member states
, EU based researchers voiced concern that if they were required under the GDPR to obtain individual's consent for the processing of their facial recognition data, a face database on the scale of MegaFace
could never be established again.
In September 2019 the Swedish Data Protection Authority
(DPA) issued its first ever financial penalty for a violation of the EU's General Data Protection Regulation
(GDPR) against a school that was using the technology to replace time-consuming roll calls during class. The DPA found that the school illegally obtained the biometric data
of its students without completing an impact assessment. In addition the school did not make the DPA aware of the pilot scheme. A 200,000 SEK fine (€19,000/$21,000) was issued.
Bans on the use of facial recognition technology
In May 2019, San Francisco
became the first major United States city to ban the use of facial recognition software for police and other local government agencies' usage.
San Francisco Supervisor, Aaron Peskin
, introduced regulations that will require agencies to gain approval from the San Francisco Board of Supervisors
to purchase surveillance
The regulations also require that agencies publicly disclose the intended use for new surveillance technology.
In June 2019, Somerville
became the first city on the East Coast
to ban face surveillance software for government use,
specifically in police investigations and municipal surveillance.
In July 2019, Oakland, California
banned the usage of facial recognition technology by city departments.
The American Civil Liberties Union
("ACLU") has campaigned across the United States for transparency in surveillance technology
and has supported both San Francisco and Somerville's ban on facial recognition software. The ACLU works to challenge the secrecy and surveillance with this technology.
In January 2020, the European Union
suggested, but then quickly scrapped, a proposed moratorium on facial recognition in public spaces.
- Berkeley, California
- Oakland, California
- Boston, Massachusetts - June 30, 2020
- Brookline, Massachusetts
- Cambridge, Massachusetts
- Northampton, Massachusetts
- Springfield, Massachusetts
- Somerville, Massachusetts
- Portland, Oregon - September, 2020
On October 27, 2020, 22 human rights groups called upon the University Of Miami
to ban facial recognition technology. This came after the students accused the school of using the software to identify student protesters. The allegations were, however, denied by the university.
In the 18th
and 19th century
the belief that facial expressions revealed the moral worth or true inner state of a human was widespread and physiognomy
was a respected science
in the Western world
. From the early 19th century onwards photography
was used in the physiognomic analysis of facial features and facial expression to detect insanity
In the 1960s and 1970s the study of human emotions and its expressions was reinvented by psychologists
, who tried to define a normal range of emotional responses to events.
The research on automated emotion recognition
has since the 1970s focused on facial expressions
, which are regarded as the two most important ways in which humans communicate emotions
to other humans. In the 1970s the Facial Action Coding System
(FACS) categorization for the physical expression of emotions was established.
Its developer Paul Ekman
maintains that there are six emotions that are universal to all human beings and that these can be coded in facial expressions.
Research into automatic emotion specific expression recognition has in the past decades focused on frontal view images of human faces.
Anti-facial recognition systems
In January 2013 Japanese researchers from the National Institute of Informatics
created 'privacy visor' glasses that use nearly infrared light to make the face underneath it unrecognizable to face recognition software.
The latest version uses a titanium frame, light-reflective material and a mask which uses angles and patterns to disrupt facial recognition technology through both absorbing and bouncing back light sources.
Some projects use adversarial machine learning
to come up with new printed patterns that confuse existing face recognition software.
Another method to protect from facial recognition systems are specific haircuts and make-up patterns that prevent the used algorithms to detect a face, known as computer vision dazzle
Incidentally, the makeup styles popular with Juggalos
can also protect against facial recognition.
Facial masks that are worn to protect from contagious viruses can reduce the accuracy of facial recognition systems. A 2020 NIST
study tested popular one-to-one matching systems and found a failure rate between five and fifty percent on masked individuals. The Verge
speculated that the accuracy rate of mass surveillance systems, which were not included in the study, would be even less accurate than the accuracy of one-to-one matching systems.
The facial recognition of Apple Pay
can work through many barriers, including heavy makeup, thick beards and even sunglasses, but fails with masks.
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Last edited on 17 May 2021, at 03:17
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