Living body recognition
In some scenes in life, we need to determine the viability of this object. However, taking photos, masks, screen shots and other means, because they are static, are very likely to make users' own accounts stolen. Therefore, in-vivo detection can protect users' legitimate rights and interests and prevent fraud. At present, there are three kinds of in-vivo detection, namely collaborative in-vivo detection, binocular in-vivo anti-counterfeiting detection and silent in-vivo detection. The most common way is to let the user blink, open his mouth, shake his head and nod, and cooperate to verify whether the user is testing himself.
Silent in-vivo detection does not need to do all kinds of actions, just take a real-time photo or video. The system can strictly check the information sent by users. Without causing video duplication. Living body identification instrument
Binocular live anti-counterfeiting detection. It is the most advanced in vivo detection method. Its principle is that the spectrum of human face skin reflection is different under different lighting conditions, and these spectra can be analyzed. Because everyone's face reflects a different spectrum, we can use special materials to distinguish the real face from the human face. This recognition technology is extremely fast. Near-infrared imaging is insensitive to illumination and can penetrate sunglasses to image, which can prevent hackers from stealing users' own biological characteristics, protect users' accounts from various forms of forged identity information, and ensure the security of remote authentication information. Living body recognition
Living body recognition is formed on the basis of algorithm, and face recognition mainly includes image capture, face key location and image preprocessing. Finally, it is identified. These recognition algorithms are based on the existing images and codes in the database, and the similarity between the input data and the existing data is obtained, and finally the recognition judgment is made.
There are four kinds of face recognition algorithms, one is based on face feature points, the other is based on the whole face image, the third is based on templates, and the fourth is based on neural networks. Some auxiliary theories include illumination estimation model theory. This preprocessing method is gray correction, which supplements illumination compensation and barefoot balance on the basis of illumination estimation model. There is also an optimized deformation statistical correction theory. Can make the face posture tend to be normal. Then the iterative theory is strengthened, which can effectively extend the DLFA face detection algorithm. There is also the identification of real-time data, which can handle the intermediate value of real-time face data. Maximum recognition efficiency can be achieved.
Living body identification technology is an important application practice of artificial intelligence, which will become more and more popular in our life.