Face recognition

What is face recognition in simple words

Face recognition is a technology that allows a computer to "recognize" a person by their face, just as you do when you see an acquaintance. Only the computer does it much faster and on a larger scale, using complex mathematical algorithms and neural networks.

The technology works in two stages: first, the system finds a face in the image and highlights key points on it (distance between the eyes, shape of the cheekbones, lip contour). Then these points are converted into a unique digital code, which is compared with the database to find a match. This is one of the fastest developing biometric technologies, which is actively being introduced into security systems, banking services and everyday devices.

Face recognition has become so accurate that in many scenarios it surpasses human capabilities. Modern neural network algorithms can recognize faces with an accuracy of more than 99%, making them a reliable tool for identification in a wide variety of conditions — from bright sunlight to poor lighting.

However, it is important to understand that face recognition is not just a "recognition" technology. It is a whole set of tasks: detecting a face in an image, highlighting key points, image normalization (eliminating the influence of lighting and angle), extracting unique features and comparing with a reference. Each of these stages requires complex mathematical calculations and powerful computing resources.

Face recognition technology is actively used in various fields: from unlocking smartphones to searching for missing people. In China, for example, face recognition systems are widely used in the metro and on the streets to ensure public safety. In Russia, the technology is used in the banking sector for remote client verification, in access control systems at enterprises and in government information systems such as the Unified Biometric System. Read about how face recognition is used in comprehensive security systems in the article ACS.

Stages of the technology

• Face detection: The system finds a face in an image or video stream. This is the first and critically important stage, since if the face is not detected, further processing is impossible. Modern detection algorithms work even on low-resolution images and with partial face occlusion.
• Alignment and normalization: The face is aligned by key points to eliminate the influence of head tilt or lighting. At this stage, the system determines the position of the eyes, nose and mouth to bring the image to a standard form. This allows comparing faces captured at different angles.
• Feature extraction: A neural network model (for example, based on CNN or Transformer) converts the face image into a unique feature vector — a "digital fingerprint" of the face. This vector contains hundreds of numeric values that describe the geometry of the face. The better the model, the more accurate the recognition will be.
• Comparison with the database: The resulting vector is compared with reference templates for identification (search among many, 1:N) or verification (one-to-one identity confirmation, 1:1). Depending on the task and database size, the comparison can take from fractions of a second to several seconds.

Each of these stages requires high accuracy and performance. Specialized neural network models trained on millions of images are used to ensure reliable operation of face recognition systems. For example, the Biovizum product is used for high-precision face recognition in access control and video surveillance systems; it implements all the listed stages with maximum efficiency.

It is worth noting that modern face recognition systems can work not only with photos, but also with real-time video streams. This allows using them to monitor large crowds, for example, at stadiums, airports or railway stations. The system can simultaneously track dozens of faces and compare them with the database, instantly signaling matches.

Application of face recognition

The technology is used everywhere: from unlocking a smartphone to monitoring law enforcement in public places. Key scenarios:

• Security and ACS: In access control systems at enterprises and secure facilities. Face recognition allows automatically identifying employees and visitors, eliminating the possibility of using someone else's passes.
• Banking: For remote client verification (KYC) when opening accounts and issuing loans. Banks use face recognition to make sure that the client submitting an application online is exactly the person they claim to be.
• Video surveillance: For searching wanted persons, crowd control and identifying violators in real time. Video surveillance systems with face recognition help law enforcement quickly find suspects.
• Government services: For identity confirmation when receiving services through the UBS. This allows citizens to receive government services remotely, without the need to visit offices.

In addition to the listed scenarios, face recognition is used in retail to analyze customer behavior, in education to monitor attendance, in healthcare to identify patients, and even in the entertainment industry — for example, in applications for creating masks and filters.

However, with the development of technology, questions of ethics and confidentiality also arise. Many experts are concerned that the mass use of face recognition could lead to violations of citizens' privacy rights. In this regard, a number of countries, including European Union countries, have introduced restrictions on the use of this technology in public places. In Russia, the use of biometric data is regulated by 152-FZ "On Personal Data", which establishes requirements for the collection, storage and processing of biometric information.

The design service includes the selection of optimal equipment and algorithms for your tasks. This is especially important, since each organization has its own unique requirements for face recognition systems: different throughput, lighting conditions, requirements for accuracy and speed. Read more about solutions in the technologies section.