What is facial recog­ni­tion?

Facial recog­ni­tion is an al­go­rith­mic process that iden­ti­fies in­di­vid­u­als or verifies their identity using unique biometric facial features. These systems provide a more efficient and accurate ver­i­fi­ca­tion process compared to tra­di­tion­al methods; however, they also present sig­nif­i­cant chal­lenges, es­pe­cial­ly con­cern­ing data pro­tec­tion.

What is a facial recog­ni­tion system?

Facial recog­ni­tion is a tech­nol­o­gy used to identify and verify in­di­vid­u­als by analyzing their unique facial features. These systems work by capturing distinct biometric markers – such as the shape of the eyes and nose – and con­vert­ing them into math­e­mat­i­cal patterns, which are then matched against a database.

Modern facial recog­ni­tion systems can identify people in photos, videos, and even in real time. This enables, for example, two images to be compared to determine if they depict the same person. Ad­di­tion­al­ly, these systems can search extensive image or video archives to locate a specific face.

How does facial recog­ni­tion work?

Facial recog­ni­tion is a multi-stage process that draws on tech­nolo­gies from the fields of computer vision and ar­ti­fi­cial in­tel­li­gence. While facial recog­ni­tion systems may vary in structure and operation, face iden­ti­fi­ca­tion generally follows this basic process:

1. Face detection: The first step is locating a face in an image or video, typically using computer vision. This tech­nol­o­gy captures facial data not only from the front but also in profile.
2. Face analysis: Next, the system analyzes the face’s biometric features. Key variables include the depth of eye sockets, the distance between the eyes, the shape of cheek­bones, and the contours of the lips, ears, and chin. Most systems use 2D images for this analysis, as these are easier to match with publicly available photos and databases.
3. Creation of a faceprint: The algorithm converts the captured facial features into a digital signature called a “faceprint”, which is a math­e­mat­i­cal rep­re­sen­ta­tion of the face. Since every person has their own facial features, this is unique, just like a fin­ger­print.
4. Com­par­i­son with database: The facial recog­ni­tion system compares the created faceprint with a database of known faces and evaluates the prob­a­bil­i­ty of a facial match. The highly developed com­par­i­son al­go­rithms achieve high accuracy despite vari­a­tions in lighting, facial ex­pres­sions, and camera angles.

What are the most important areas of ap­pli­ca­tion for facial recog­ni­tion systems?

Facial recog­ni­tion tech­nolo­gies are now used for a wide range of ap­pli­ca­tions. The most important areas of ap­pli­ca­tion include:

• Smart­phones: Many smart­phones now offer facial recog­ni­tion as an option for unlocking the device. According to Apple’s statement on “Face ID”, the prob­a­bil­i­ty of a random face unlocking an iPhone is less than one in a million.
• Law en­force­ment: In the US and other countries, facial recog­ni­tion is in­creas­ing­ly used to locate in­di­vid­u­als wanted by the police. Officers can even use mobile devices on-site to take a photo and compare it with databases in real time.
• Airports and border controls: A growing number of travelers carry biometric passports, allowing them to bypass long queues with ePassport control. Facial recog­ni­tion is also deployed at major events, like the Olympic Games, to enhance security.
• Banking: The banking apps of many financial in­sti­tu­tions allow users to au­then­ti­cate trans­ac­tions using facial recog­ni­tion. As no password or PIN needs to be entered, cyber criminals have no op­por­tu­ni­ty to capture relevant data. This increases the security of online banking.
• Health­care: A facial recog­ni­tion system can be used to stream­line patient reg­is­tra­tion in hospitals. Facial recog­ni­tion also makes it possible to recognize emotions and pain in the people being treated.

Five practical ap­pli­ca­tion examples for facial recog­ni­tion

• The e-commerce giant Amazon has developed a cloud-based facial recog­ni­tion system called Rekog­ni­tion. Beyond face-based user ver­i­fi­ca­tion, it supports mood analysis and can scan videos to flag po­ten­tial­ly offensive content.
• The tech company Apple allows its customers to unlock their smart­phone using facial recog­ni­tion. It’s also possible to use facial recog­ni­tion to log into apps and confirm purchases.
• British Airways enables travelers (depending on the airport) to verify their identity via facial recog­ni­tion. This elim­i­nates the need to show your passport or boarding pass.
• Coca Cola uses facial recog­ni­tion, among other things, to reward customers in China for recycling bottles and cans. In Australia, the company displays per­son­al­ized ad­ver­tis­ing on its vending machines and in Israel, facial recog­ni­tion is used in con­nec­tion with event marketing.
• The social media platform Facebook has been using a facial recog­ni­tion tool in the USA since 2010 to au­to­mat­i­cal­ly tag people in photos (only on a voluntary basis since 2019).

What’s the role of ar­ti­fi­cial in­tel­li­gence in facial recog­ni­tion?

Ar­ti­fi­cial in­tel­li­gence is essential to the de­vel­op­ment and operation of modern facial recog­ni­tion systems. AI tools enable the con­tin­u­ous im­prove­ment of tech­nol­o­gy through machine learning. Cor­re­spond­ing systems use the data provided to adapt their al­go­rithms and thus become in­creas­ing­ly efficient over time.

Neural networks form the basis of modern facial recog­ni­tion systems. So-called con­vo­lu­tion­al neural networks (CNNs) are used to process facial images in stages, creating highly accurate faceprints even in sub­op­ti­mal con­di­tions. CNNs can perform this pro­cess­ing in real time, making them par­tic­u­lar­ly valuable for security-critical ap­pli­ca­tions such as access control and sur­veil­lance systems.

What op­por­tu­ni­ties and risks does the use of facial recog­ni­tion entail?

Facial recog­ni­tion offers con­sid­er­able potential, par­tic­u­lar­ly in the areas of security and ef­fi­cien­cy. Today’s gen­er­a­tion of facial recog­ni­tion systems enable both fast and reliable iden­ti­fi­ca­tion of people, which is useful for access control as well as for fighting crime and solving criminal offenses. Facial recog­ni­tion also improves the user ex­pe­ri­ence – for example as an unlocking option for smart­phones. Moreover, facial recog­ni­tion allows companies to provide per­son­al­ized services and stream­line processes.

The primary risks as­so­ci­at­ed with facial recog­ni­tion revolve around data pro­tec­tion and privacy. These systems enable in­di­vid­u­als to be iden­ti­fied and monitored without their knowledge, raising the risk of misuse by gov­ern­ments, companies, and cy­ber­crim­i­nals. Ad­di­tion­al­ly, experts have voiced concerns about the accuracy of facial recog­ni­tion, par­tic­u­lar­ly regarding ethnic mi­nori­ties, where misiden­ti­fi­ca­tion occurs more fre­quent­ly.