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.
Facial recognition is a biometric identification process. These methods are characterized by the fact that they use unique, distinguishable features to identify people. In addition to facial recognition, voice recognition, fingerprint recognition and eye recognition also fall into this category.
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:
- 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.
- 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.
- 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.
- 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.
2D face recognition systems are primarily used to analyze images because they are easier to implement and more cost-effective. 3D face recognition, on the other hand, incorporates depth information, allowing it to identify faces from various angles and under challenging lighting conditions. This enhances accuracy but also increases complexity and cost.
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.
- Get online faster with AI tools
- Fast-track growth with AI marketing
- Save time, maximize results
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.
It can be assumed that future developments in facial recognition will enhance accuracy and reliability, particularly through advancements in artificial intelligence and machine learning. New applications are likely to emerge, especially in areas such as augmented reality and smart cities. To prevent misuse, a critical challenge will be ensuring that regulations and ethical standards evolve in tandem with technological advancements.