Face recognition technology features and technical difficulties

The main feature of face recognition is the use of human facial features as an identity recognition method. By collecting images or video streams that contain faces, the human face in images or videos is automatically detected, positioned, preprocessed, and characterized. Extract and match the recognition process to achieve the purpose of identifying different people. Therefore, using this feature of face recognition technology can achieve a variety of intelligent applications in different situations.

The advantages of face recognition include the following four aspects:

(1) Nature. The so-called natural nature refers to the ability to distinguish and confirm identities by comparing human faces through observation.

(2) Non-mandatory. The identified face image information can be actively acquired without being detected by the measured individual and is hidden from the individual.

(3) Non-contact. Compared to other biometric technologies, face recognition is non-contact and users do not need to be in direct contact with the device.

(4) Concurrency. In practical application scenarios, face recognition technology can perform multiple face sorting, judgment, and recognition.

Difficulties in face recognition technology

(1) Image lighting problem

The identified videos and pictures face the challenges of various ambient light sources. Sidelights, top lights, backlights, and highlights may appear, and there may be different lighting conditions at different times. Even the lights in different locations within the monitoring area are different.

(2) Face poses and accessories issues

Because the monitoring is non-matching, the monitoring personnel pass through the monitoring area in a natural posture, and therefore various non-normal face postures such as side-face, head-down, and head-up may occur, and accessories such as hats, black-rimmed glasses, and masks may appear.

(3) Similarities in human faces

The differences between different individuals, especially the same people, are not significant. The structure of all human faces is similar, and even the structure of human faces is very similar. Such a feature is advantageous for locating a person's face, but is disadvantageous for distinguishing individuals using a person's face. The masking of makeup and the natural similarity of the twins add to the difficulty of recognition.

(4) Face variability

The appearance of the human face is very unstable, and people can produce a lot of expressions through the changes of the face, and the visual images of the faces vary greatly from different viewing angles.

With the increase in the number of targets to be identified and the probability of people with more similar images, the existing deep learning technology has greatly improved in these areas. At present, the face recognition technology of many companies has been implemented in the LFW evaluation. 99.5% or more, and some approaches or even exceed the recognition rate of human eyes. This provides technical support for the large-scale practical application of face recognition systems. With the continuous advancement of science and technology, it is expected that the challenges in the face recognition field will be solved in the future.