Face recognition technology is a method of capturing an image or video stream containing a face with a camera or a camera, and automatically detecting and tracking the face in the image, thereby performing a series of comparison, human-based faces on the detected face. The biometric technology for identifying the feature information is usually called portrait recognition or face recognition.
The features extracted from the face to be recognized are compared with the features of the face in the database, and the classification is determined according to the similarity. Face recognition can be divided into two major categories: one is confirmation, which is the process of comparing the face image with the existing image of the person in the database, answering whether you are your problem; the other is identifying This is the process of matching the face image with all the images already stored in the database, answering who you are. Obviously, face recognition is more difficult than face recognition because identification requires a large amount of data matching.
Similar to the fingerprint application method, the face recognition technology is relatively mature and is also an attendance machine. Because in the attendance system, the user is actively cooperating, and the qualified face can be obtained in a specific environment. This provides a good input source for face recognition and often results in satisfactory results. However, in some public places, video surveillance probes, due to light and angle problems, the resulting face images are difficult to compare successfully. This is also one of the problems that must be solved in the future development of face recognition technology.
1. Face detection: According to the characteristics of the eyes, eyebrows, mouth, nose and other organs and the geometric positional relationship between them, the face is detected, that is, whether an image is present in an image or a series of images (such as video). If there is, return the size, position and other information of the face.
2. Face image preprocessing: The original graphics acquired by the system must be subjected to grayscale correction and noise filtering image preprocessing in the early stage of image processing due to various conditions and random interference. Face image preprocessing mainly includes: face alignment, face image enhancement, and normalization.
3. Face image feature extraction: Face recognition extraction is performed on certain features of the face. The face feature is also called face representation, which is the process of character modeling the face.
4. Face image matching and recognition: Face recognition is to compare the face feature to be recognized with the feature template of the obtained face, and judge the identity information of the face according to the degree of similarity. This process is divided into two categories: one is face recognition, which is a one-to-one process of image comparison. According to the degree of similarity, it is judged whether it is the same person. The degree of similarity is generally based on whether the quantitative threshold is exceeded. .
The other type is face recognition. It is a one-to-many image comparison. It compares a person's face with the face of a multi-person in the database, and identifies the person's identity based on the comparison result, or finds the most similar one. The face is output and the test result is output according to the degree of similarity. The brush face registration system in Wuzhen Internet Conference, the gate of Baidu Building, people can stay in 1 to 2 seconds, which is 1:N face recognition.
Semiconductor Moly
Semiconductor Moly
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