What is Facial Recognition System

Facial recognition is a way of identifying or confirming your identity using your face. Facial recognition systems can be used to identify people in photos, videos, or in real-time.

Facial recognition has historically worked like other forms of “biometric” identification such as speech recognition, the irises of your eyes, or fingerprint identification.

Fingerprint data, for example, is gathered and analyzed for identifying markers. A newly found fingerprint can then be evaluated against this database for matching markers.

Facial recognition works in the same way. A computer analyzes image data and looks for a very specific set of markers within it – everything from a person’s head shape to the depth of their eye sockets.

A database of facial markers is created, and an image of a face that shares a critical threshold of similarity from the database indicates a possible match. This is the basic principle behind all types of facial recognition, from unlocking your iPhone by scanning your face, to intercepting known shoplifters as they enter a store.

That worked well enough for relatively simple jobs, like figuring out where faces were within a photo, but to identify a particular face as matching a photograph of the same person? That turned out to be a bit more difficult.


How does facial recognition work?

You might be good at recognizing faces. You probably find it a cinch to identify the face of a family member, friend, or acquaintance. You’re familiar with their facial features — their eyes, nose, mouth — and how they come together.

That’s how a facial recognition system works, but on a grand, algorithmic scale. Where you see a face, recognition technology sees data. That data can be stored and accessed. For instance, half of all American adults have their images stored in one or more facial-recognition databases that law enforcement agencies can search, according to a Georgetown University study.

So how does facial recognition work? Technologies vary, but here are the basic steps:

Step 1. A picture of your face is captured from a photo or video. Your face might appear alone or in a crowd. Your image may show you looking straight ahead or nearly in profile.

Step 2. Facial recognition software reads the geometry of your face. Key factors include the distance between your eyes and the distance from forehead to chin. The software identifies facial landmarks — one system identifies 68 of them — that are key to distinguishing your face. The result: your facial signature.

Step 3. Your facial signature — a mathematical formula — is compared to a database of known faces. And consider this: At least 117 million Americans have images of their faces in one or more police databases. According to a May 2018 report, the FBI has had access to 412 million facial images for searches.

Step 4. A determination is made. Your faceprint may match that of an image in a facial recognition system database.

History of Facial Recognition Technology

Earlier facial recognition technology was considered as an idea of science fiction. But in the past decade, facial recognition technology has not only become real — but it’s widespread. Today, people can easily read articles and news stories about facial recognition everywhere. Here is the history of facial recognition technology and some ideas about its bright future.

Facial recognition technology along with AI (Artificial Intelligence) and Deep Learning (DL) technology is benefiting several industries. These industries include law enforcement agencies, airports, mobile phone manufacturing companies, home appliance manufacturing companies, etc.

Nowadays even retailers are using AI-based facial recognition technology to prevent violence and crime. Airports are getting better-secured environments, mobile phone makers are using face recognition to bring the biometric security feature in the devices.

For Brief history of facial Recognition Technology, visit here

How accurate is facial recognition?

In ideal conditions, facial recognition systems can have near-perfect accuracy. Verification algorithms used to match subjects to clear reference images (like a passport photo or mugshot) can achieve accuracy scores as high as 99.97% on standard assessments like NIST’s Facial Recognition Vendor Test (FRVT). This is comparable to the best results of iris scanners. This kind of face verification has become so reliable that even banks feel comfortable relying on it to log users into their accounts.

However, this degree of accuracy is only possible in ideal conditions where there is consistency in lighting and positioning, and where the facial features of the subjects are clear and unobscured. In real-world deployments, accuracy rates tend to be far lower.  For example, the FRVT found that the error rate for one leading algorithm climbed from 0.1% when matching against high-quality mugshots to 9.3% when matching instead to pictures of individuals captured “in the wild,” where the subject may not be looking directly at the camera or maybe obscured by objects or shadows. Aging is another factor that can severely impact error rates, as changes in subjects’ faces over time can make it difficult to match pictures taken many years apart. NIST’s FRVT found that many middle-tier algorithms showed error rates increasing by almost a factor of 10 when attempting to match to photos taken 18 years prior.

Sensitivity to external factors can be most clearly seen when considering how facial recognition algorithms perform on matching faces recorded in surveillance footage. NIST’s 2017 Face in Video Evaluation (FIVE) tested algorithms’ performance when applied to video captured in settings like airport boarding gates and sports venues. The test found that when using footage of passengers entering through boarding gates—a relatively controlled setting—the best algorithm had an accuracy rate of 94.4%. In contrast, leading algorithms identifying individuals walking through a sporting venue—a much more challenging environment—had accuracies ranging between 36% and 87%, depending on camera placement.

The FIVE results also demonstrate another major issue with facial recognition accuracy—the wide variation between vendors. Though one top algorithm achieved 87% accuracy at the sporting venue, the median algorithm achieved just 40% accuracy working off imagery from the same camera. NIST’s tests on image verification algorithms found that many facial recognition providers on the market may have error rates several orders of magnitude higher than the leaders. Though some vendors have constructed highly accurate facial recognition algorithms, the average provider on the market still struggles to achieve similar reliability, and even the best algorithms are highly sensitive to external factors. According to NIST, this large accuracy range between vendors “indicates that face recognition software is far from being commoditized.”

How facial recognition is used?

The technology has a wide range of applications. These are some of them:

Unlocking phones

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 phone is stolen. Apple claims that the chance of a random face unlocking your phone is about one in 1 million.

Law enforcement

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 collect 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 one or more face recognition databases to attempt an identification.

Airports and border control

Facial recognition has become a familiar sight at many airports around the world. Increasing numbers of travelers 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 travelers by 2023. As well as at airports and border crossings, the technology is used to enhance security at large-scale events such as the Olympics.

Finding missing persons

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.

Reducing retail crime

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.

Improving retail experiences

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, ‘liveness’ 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.

Marketing and advertising

Marketers have used facial recognition to enhance consumer experiences. For example, frozen pizza brand DiGiorno used facial recognition for a 2017 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.

Tracking student or worker attendance

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.

Recognizing drivers

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.

Monitoring gambling addictions

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.

Advantages of face recognition

  • Increased security

  • Reduced crime

  • Removing bias from stop and search

  • Greater convenience

  • Fast and Non-Invasive Identity Verification

  • Integration with other technologies

Disadvantages of face recognition

  • Cons of Facial Recognition on Society

  •  Individual Privacy Concerns

  • Data Privacy Concern with Facial Recognition

  •  Facial Recognition and Racial Bias

  • Scope for error

  • Massive data storage

Facial Recognition Problems

Biometric face recognition has been implemented in many industries, but they are still met with some skepticism. Concerns against this monitoring technology include errors in recognition, privacy, and misuse of data.

Identification Errors

Facial Recognition technology doesn’t always work as well as it should. Facial Recognition systems can be impacted by poor lighting or low image quality. The data may not match up with the person’s nodal points because of camera angles being obscured; this creates an error when matching faceprints cannot be verified in the database.


Privacy issues exist with facial recognition technology because of the ability to identify and track whereabouts, which may constitute an invasion of rights. Facial recognition technology can track individuals, which makes people uneasy. In addition, significant data breaches are all too common these days, and the personal information that facial recognition software collects is not immune.

Misuse of Data

Although facial recognition technology has been used for decades, mistrust comes from how the data will be used ethically. Facial recognition can be applied to captured video footage of public and private spaces. Privacy advocates continue to view this as an invasion of privacy because many organizations lack accountability when data breaches occur.

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