Aug. 11, 2025
Current top-performing facial recognition algorithms provide prompt, high-confidence matches when the probe image is obtained cooperatively and when the reference image is of high quality. Under these conditions and using today’s best face recognition algorithms, 99.9 percent of searches with a sufficiently clear face image will return the correct matching entry in a government database of 12 million identities in under a second.
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Two key performance metrics are FP and FN match rates.
Matching performance will be worse when the probe image is obtained under suboptimal conditions (e.g., poor lighting) or when the reference image is outdated or of low resolution or contrast. Nevertheless, with the best available algorithms, as long as both the eyes in a face can be automatically detected, a probe image can be matched to an individual with more than 99 percent accuracy.1 In many cases, even if only one eye can be detected, an image of an individual can still be matched with high accuracy; even profile-view images can often be correctly matched.
Much progress has been made in recent years to characterize, understand, and mitigate phenotypical disparities in the accuracy of FRT results. However, these
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performance differentials have not been entirely eliminated, even in the most accurate existing algorithms. FRT still performs less well for individuals with certain phenotypes, including those typically distinguished on the basis of race, ethnicity, or gender.
Tests show that FN rate differentials are extremely small if both the probe and reference images are of high quality, but the differentials can become significant if they are not. FN matches occur when the similarity score between two different images of one person is low. Causes include changes in appearance and loss of detail from poor image contrast. FN match rates vary across algorithms and have been measured to be higher by as much as a factor of 3 in women, Africans, and African Americans than in Whites. The most accurate algorithms also generally have the lowest demographic variance. FN match rate disparities are highest in applications where the photographic conditions cannot be controlled and can be reduced with better photography and better comparison algorithms. The consequences of an FN match include a failure to identify the subject of an investigation or the need for an individual to identify themselves in another way, such as by presenting identity documents. Rate disparities mean, for example, that the burden of presenting identification falls disproportionately on some groups of individuals—including groups that have been historically disadvantaged and marginalized. Although this additional time and inconvenience may be seemingly small in a single instance, the aggregate impacts to individuals who repeatedly encounter it and to groups disproportionately affected can be large.
FP matches occur when the similarity score between images of two different people is high. (The likelihood of an FP can thus be reduced with a higher similarity threshold.) Higher FP match rates are seen with women, older subjects, and—for FRT algorithms designed and trained in the West—individuals of East Asian, South Asian, and African descent. However, some Chinese-developed algorithms have the lowest FP rates for East Asian subjects. FP match rate differences occur even when the images are of very high quality and can vary across demographic groups markedly and contrary to the intent of the developer. FP match rate disparities can be reduced using more diverse data to train models used to create templates from facial images or model training with a loss function that more evenly clusters but separates demographic groups. The applications most affected by FP match rate differentials are those using large galleries and where most searches are for individuals who are not present in the gallery. FP rate disparities will mean that members of some groups bear an unequal burden of, for example, being falsely identified as the target of an investigation.
Tests also show that for identity verification (one-to-one comparison) algorithms, the FP match rates for certain demographic groups, when using even the best performing facial recognition algorithms designed in Western countries and trained mostly on White faces, are relatively higher (albeit very low in absolute terms), even if both the probe and reference images are of high quality.
A final concern with FPs is that as the size of reference galleries or the rate of queries increases, the possibility of an FP match grows, as there are more potential templates that can return a high similarity score to a probe face. Some face recognition algorithms, however, adjust similarity scores in an attempt to make the FP match rate independent of the gallery size.
RECOMMENDATION 1: The federal government should take prompt action along the lines of Recommendations 1-1 through 1-6 to mitigate against potential harms of facial recognition technology and lay the groundwork for more comprehensive action.
RECOMMENDATION 1-1: The National Institute of Standards and Technology should sustain a vigorous program of facial recognition technology testing and evaluation to drive continued improvements in accuracy and reduction in demographic biases.
Testing and standards are a valuable tool for driving performance improvements and establishing appropriate testing protocols and performance benchmarks, providing a firmer basis for justified public confidence, for example, by establishing an agreed-on baseline of performance that a technology must meet before it is deployed. The National Institute of Standards and Technology’s (NIST’s) Facial Recognition Technology Evaluation has proven to be a valuable tool for assessing and thereby propelling advances in FRT performance, including by increasing accuracy and reducing demographic differentials. This work, and the trust it has engendered, provide the foundation for NIST to take on an expanded role in developing needed standards in such areas as evaluating and reporting on performance, minimum image quality, data security, and quality control.
NIST would be a logical home for such activities within the federal government given its role in measurement and standards generally and FRT evaluation specifically.
Organizations deploying FRTs face a complex set of trade-offs and considerations as they seek to use the technology fairly and effectively. To help manage these complex tradeoffs around privacy, equity, civil liberties, and technical performance, a framework that is specified in advance can help users identify and manage risks, define appropriate measures to protect privacy, ensure transparency and effective human oversight, and identify and mitigate concerns around equity. A framework can similarly assist bodies charged with oversight of FRTs, whether governmental agencies or civil society organizations, in making decisions about where the use of FRTs is appropriate and where it should be constrained. Such a framework could also form the basis for future mandatory disclosure laws or regulations.
RECOMMENDATION 1-5: The federal government should establish a program to develop and refine a risk management framework to help organizations identify and mitigate the risks of proposed facial recognition technology applications with regard to performance, equity, privacy, civil liberties, and effective governance.
Risk management frameworks are a valuable tool for identifying and managing risks, defining appropriate measures to protect privacy, ensuring transparency and effective human oversight, and identifying and mitigating concerns around equity. A risk management framework could also form the basis for future mandatory disclosure laws or regulations.2 Current examples of federally defined risk management frameworks
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3.7.2 Do the training or certification regimes adequately mitigate the risks of the system usage?
3.8 Human-in-the-loop
3.8.1 Is an identified individual responsible for all significant decisions or actions made on the basis of an FRT match result?
3.9 Accountability
3.9.1 Which is the expected/positive outcome or adverse outcome for an individual? What is the cost or consequence to an individual of an adverse outcome?
3.9.2 Are appropriate (i.e., commensurate with cost/consequence) recourse/redress mechanisms available to individuals who will experience adverse outcomes?
3.9.3 Does the organization using FRT have a mechanism for receiving complaints? Is it easy for individuals experiencing issues with the FRT system to find and use the complaint mechanism?
Note that some of the issues in this list cut across most if not all use cases, while others depend on the particular use case.
The framework outlined in the preceding section is intended to identify issues that arise from the use of FRT in specific contexts. This section provides some examples of how the questions delineated in the risk management framework may provide helpful insight in concrete use cases. This section therefore applies portions of the risk management framework to four of the use cases introduced in Chapter 3—employee access control, aircraft boarding, protest surveillance, and retail loss prevention—to illustrate how the general questions posed in the framework play out in the context of specific uses and to develop a set of potential best practices for each case. These illustrative applications are brief and certainly do not consider every element of the risk framework, but they are intended to illustrate how a risk framework such as that suggested above can draw attention, in particular use cases, to key design and use issues that may enhance or detract from important values, like privacy and transparency. Encouraging (or requiring) that a framework be used to assess any given FRT invites organizations to, in essence, “show their work” and thus enhances transparency and, in many instances, can lead to greater care in system design.
Such practices will help address mistrust about bias in FRT’s technological underpinnings and broader mistrust, especially in minority communities, about the role of technology in law enforcement and similar contexts.
An outright ban on all FRT under any condition is not practically achievable, may not necessarily be desirable to all, and is in any event an implausible policy, but restrictions or other regulations are appropriate for particular use cases and contexts.
Concerns about the impacts of FRT intersect with wider questions about how to protect consumer privacy, where and how to limit government surveillance that could infringe on civil liberties, and more generally how to govern and regulate a proliferation of artificial intelligence and other powerful computing technologies.
RECOMMENDATION 3: The Executive Office of the President should consider issuing an executive order on the development of guidelines for the appropriate use of facial recognition technology by federal departments and agencies and addressing equity concerns and the protection of privacy and civil liberties.
Comprehensively addressing such questions, especially to address nongovernmental uses, may require new federal legislation.
In light of the fact that FRT has the potential for mass surveillance of the population, courts and legislatures will need to consider the implications for constitutional protections related to surveillance, such as due process and search and seizure thresholds and free speech and assembly rights.
In grappling with these issues, courts and legislatures will have to consider such factors as who uses FRT, where it is used, what is it being used for, under what circumstances it is appropriate to use FRT-derived information provided by third parties, whether its use is based on individualized suspicion, intended and unintended consequences, and susceptibility to abuse, while courts will have to determine how constitutional guarantees around due process, privacy, and civil liberties apply the deployment of FRT.
As governments and other institutions take affirmative steps through both law and policy to ensure the responsible use of FRT, they will need to take into account the views of government oversight bodies, civil society organizations, and affected communities to develop appropriate safeguards.
Facial recognition is a way of identifying or confirming an individual’s identity using their face. Facial recognition systems can be used to identify people in photos, videos, or in real-time.
Facial recognition is a category of biometric security. Other forms of biometric software include voice recognition, fingerprint recognition, and eye retina or iris recognition. The technology is mostly used for security and law enforcement, though there is increasing interest in other areas of use.
Many people are familiar with face recognition technology through the FaceID used to unlock iPhones (however, this is only one application of face recognition). Typically, facial recognition does not rely on a massive database of photos to determine an individual’s identity — it simply identifies and recognizes one person as the sole owner of the device, while limiting access to others.
Beyond unlocking phones, facial recognition works by matching the faces of people walking past special cameras, to images of people on a watch list. The watch lists can contain pictures of anyone, including people who are not suspected of any wrongdoing, and the images can come from anywhere — even from our social media accounts. Facial technology systems can vary, but in general, they tend to operate as follows:
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The camera detects and locates the image of a face, either alone or in a crowd. The image may show the person looking straight ahead or in profile.
Next, an image of the face is captured and analyzed. Most facial recognition technology relies on 2D rather than 3D images because it can more conveniently match a 2D image with public photos or those in a database. The software reads the geometry of your face. Key factors include the distance between your eyes, the depth of your eye sockets, the distance from forehead to chin, the shape of your cheekbones, and the contour of the lips, ears, and chin. The aim is to identify the facial landmarks that are key to distinguishing your face.
The face capture process transforms analog information (a face) into a set of digital information (data) based on the person's facial features. Your face's analysis is essentially turned into a mathematical formula. The numerical code is called a faceprint. In the same way that thumbprints are unique, each person has their own faceprint.
Your faceprint is then compared against a database of other known faces. For example, the FBI has access to up to 650 million photos, drawn from various state databases. On Facebook, any photo tagged with a person’s name becomes a part of Facebook's database, which may also be used for facial recognition. If your faceprint matches an image in a facial recognition database, then a determination is made.
Of all the biometric measurements, facial recognition is considered the most natural. Intuitively, this makes sense, since we typically recognize ourselves and others by looking at faces, rather than thumbprints and irises. It is estimated that over half of the world's population is touched by facial recognition technology regularly.
The technology is used for a variety of purposes. These include:
Various phones, including the most recent iPhones, use face recognition to unlock the device. The technology offers a powerful way to protect personal data and ensures that sensitive data remains inaccessible if the is stolen. Apple claims that the chance of a random face unlocking your is about one in 1 million.
Facial recognition is regularly being used by law enforcement. According to this NBC report, the technology is increasing amongst law enforcement agencies within the US, and the same is true in other countries. Police collects mugshots from arrestees and compare them against local, state, and federal face recognition databases. Once an arrestee’s photo has been taken, their picture will be added to databases to be scanned whenever police carry out another criminal search.
Also, mobile face recognition allows officers to use smartphones, tablets, or other portable devices to take a photo of a driver or a pedestrian in the field and immediately compare that photo against to one or more face recognition databases to attempt an identification.
Facial recognition has become a familiar sight at many airports around the world. Increasing numbers of travellers hold biometric passports, which allow them to skip the ordinarily long lines and instead walk through an automated ePassport control to reach the gate faster. Facial recognition not only reduces waiting times but also allows airports to improve security. The US Department of Homeland Security predicts that facial recognition will be used on 97% of travellers by . As well as at airports and border crossings, the technology is used to enhance security at large-scale events such as the Olympics.
Facial recognition can be used to find missing persons and victims of human trafficking. Suppose missing individuals are added to a database. In that case, law enforcement can be alerted as soon as they are recognized by face recognition — whether it is in an airport, retail store, or other public space.
Facial recognition is used to identify when known shoplifters, organized retail criminals, or people with a history of fraud enter stores. Photographs of individuals can be matched against large databases of criminals so that loss prevention and retail security professionals can be notified when shoppers who potentially represent a threat enter the store.
The technology offers the potential to improve retail experiences for customers. For example, kiosks in stores could recognize customers, make product suggestions based on their purchase history, and point them in the right direction. “Face pay” technology could allow shoppers to skip long checkout lines with slower payment methods.
Biometric online banking is another benefit of face recognition. Instead of using one-time passwords, customers can authorize transactions by looking at their smartphone or computer. With facial recognition, there are no passwords for hackers to compromise. If hackers steal your photo database, 'liveless' detection – a technique used to determine whether the source of a biometric sample is a live human being or a fake representation – should (in theory) prevent them from using it for impersonation purposes. Face recognition could make debit cards and signatures a thing of the past.
Marketers have used facial recognition to enhance consumer experiences. For example, frozen pizza brand DiGiorno used facial recognition for a marketing campaign where it analyzed the expressions of people at DiGiorno-themed parties to gauge people’s emotional reactions to pizza. Media companies also use facial recognition to test audience reaction to movie trailers, characters in TV pilots, and optimal placement of TV promotions. Billboards that incorporate face recognition technology – such as London’s Piccadilly Circus – means brands can trigger tailored advertisements.
Hospitals use facial recognition to help with patient care. Healthcare providers are testing the use of facial recognition to access patient records, streamline patient registration, detect emotion and pain in patients, and even help to identify specific genetic diseases. AiCure has developed an app that uses facial recognition to ensure that people take their medication as prescribed. As biometric technology becomes less expensive, adoption within the healthcare sector is expected to increase.
Some educational institutions in China use face recognition to ensure students are not skipping class. Tablets are used to scan students' faces and match them to photos in a database to validate their identities. More broadly, the technology can be used for workers to sign in and out of their workplaces, so that employers can track attendance.
According to this consumer report, car companies are experimenting with facial recognition to replace car keys. The technology would replace the key to access and start the car and remember drivers’ preferences for seat and mirror positions and radio station presets.
Facial recognition can help gambling companies protect their customers to a higher degree. Monitoring those entering and moving around gambling areas is difficult for human staff, especially in large crowded spaces such as casinos. Facial recognition technology enables companies to identify those who are registered as gambling addicts and keeps a record of their play so staff can advise when it is time to stop. Casinos can face hefty fines if gamblers on voluntary exclusion lists are caught gambling.
Technology companies that provide facial recognition technology include:
Aside from unlocking your smartphone, facial recognition brings other benefits:
On a governmental level, facial recognition can help to identify terrorists or other criminals. On a personal level, facial recognition can be used as a security tool for locking personal devices and for personal surveillance cameras.
Face recognition makes it easier to track down burglars, thieves, and trespassers. The sole knowledge of the presence of a face recognition system can serve as a deterrence, especially to petty crime. Aside from physical security, there are benefits to cybersecurity as well. Companies can use face recognition technology as a substitute for passwords to access computers. In theory, the technology cannot be hacked as there is nothing to steal or change, as is the case with a password.
Public concern over unjustified stops and searches is a source of controversy for the police — facial recognition technology could improve the process. By singling out suspects among crowds through an automated rather than human process, face recognition technology could help reduce potential bias and decrease stops and searches on law-abiding citizens.
As the technology becomes more widespread, customers will be able to pay in stores using their face, rather than pulling out their credit cards or cash. This could save time in checkout lines. Since there is no contact required for facial recognition as there is with fingerprinting or other security measures – useful in the post-COVID world – facial recognition offers a quick, automatic, and seamless verification experience.
The process of recognizing a face takes only a second, which has benefits for the companies that use facial recognition. In an era of cyber-attacks and advanced hacking tools, companies need both secure and fast technologies. Facial recognition enables quick and efficient verification of a person’s identity.
Most facial recognition solutions are compatible with most security software. In fact, it is easily integrated. This limits the amount of additional investment required to implement it.
While some people do not mind being filmed in public and do not object to the use of facial recognition where there is a clear benefit or rationale, the technology can inspire intense reactions from others. Some of the disadvantages or concerns include:
Some worry that the use of facial recognition along with ubiquitous video cameras, artificial intelligence, and data analytics creates the potential for mass surveillance, which could restrict individual freedom. While facial recognition technology allows governments to track down criminals, it could also allow them to track down ordinary and innocent people at any time.
Facial recognition data is not free from error, which could lead to people being implicated for crimes they have not committed. For example, a slight change in camera angle or a change in appearance, such as a new hairstyle, could lead to error. In , Newsweek reported that Amazon’s facial recognition technology had falsely identified 28 members of the US Congress as people arrested for crimes.
The question of ethics and privacy is the most contentious one. Governments have been known to store several citizens' pictures without their consent. In , the European Commission said it was considering a ban on facial recognition technology in public spaces for up to five years, to allow time to work out a regulatory framework to prevent privacy and ethical abuses.
Facial recognition software relies on machine learning technology, which requires massive data sets to “learn” to deliver accurate results. Such large data sets require robust data storage. Small and medium-sized companies may not have sufficient resources to store the required data.
While biometric data is generally considered one of the most reliable authentication methods, it also carries significant risk. That’s because if someone’s credit card details are hacked, that person has the option to freeze their credit and take steps to change the personal information that was breached. What do you do if you lose your digital ‘face’?
Around the world, biometric information is being captured, stored, and analyzed in increasing quantities, often by organizations and governments, with a mixed record on cybersecurity. A question increasingly being asked is, how safe is the infrastructure that holds and processes all this data?
As facial recognition software is still in its relative infancy, the laws governing this area are evolving (and sometimes non-existent). Regular citizens whose information is compromised have relatively few legal avenues to pursue. Cybercriminals often elude the authorities or are sentenced years after the fact, while their victims receive no compensation and are left to fend for themselves.
As the use of facial recognition becomes more widespread, the scope for hackers to steal your facial data to commit fraud — increases.
A comprehensive cybersecurity package is an essential part of protecting your online privacy and security. We recommend Kaspersky Security Cloud which provides protection for all your devices and includes antivirus, anti-ransomware, mobile security, password management, VPN, and parental controls.
Biometric technology offers very compelling security solutions. Despite the risks, the systems are convenient and hard to duplicate. These systems will continue to develop in the future — the challenge will be to maximize their benefits while minimizing their risks.
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