What is the principle of live identification? How is it done? Let’s take a look next.
Liveness recognition In some scenes in life, we need to determine the viability of the object. As methods such as taking photos, masks, and screenshots are static, it is very likely that the user's account will be stolen. Therefore, liveness detection can protect the legitimate rights and interests of users and prevent fraud. Currently, there are three types of living body detection, cooperative living body detection, binocular live body anti-counterfeiting detection and silent living body detection. The most common way is to ask the user to blink, open his mouth, shake his head, nod, and cooperate to verify whether it is the user himself who is testing.
Silent live body detection does not require various actions, just take a real-time photo or video. The system can strictly check the information sent by users. This will not cause the video to be duplicated.
Binocular anti-counterfeiting detection of living body identification equipment. It is the most advanced living body detection method. Its principle is that human facial skin reflects different spectra under different lighting conditions, and these spectra can be analyzed. Since the spectrum reflected by each person's face is also different, real faces and faces made of special materials can be distinguished. This recognition technology is extremely fast. Near-infrared imaging is not sensitive to light and can penetrate sunglasses for imaging. It can prevent hackers from stealing the user's biometrics, protect the user's account from various forms of forged identity information, and ensure the security of remote verification information.
Live recognition
Live recognition is based on algorithms. Face recognition mainly includes image capture, positioning of key parts of the face, and preprocessing of the image. Finally, recognition is carried out. These recognition algorithms are based on the existing images and codes in the database to obtain the similarity between the input data and the existing data, and finally make the recognition judgment.
There are four classifications of face recognition algorithms, one is based on facial feature points, the second is based on the entire face image, the third is based on templates, and the fourth is based on neural networks. Some auxiliary theories include the theory of illumination estimation model. This preprocessing method is grayscale correction. On the basis of the illumination estimation model, illumination compensation and barefoot balance are supplemented. There is also an optimized statistical correction theory of deformation. It can normalize the posture of the human face. Next is the enhanced iteration theory, which can effectively expand the DLFA face detection algorithm. There is also real-time data recognition, which can process the intermediate value of real-time face data. The identification efficiency can be maximized.
Liveness recognition technology is an important application practice in artificial intelligence and will become more and more widely used in our lives.