Multi-model multi-channel face identification method and system

Abstract

The invention relates to a multi-model multi-channel face identification method and system, belongs to the technical field of face identification. Different face identifications are used to generate different face characteristics, the different characteristics include different information, and the characteristics are fused to carry out face identification, so that the identification rate can be improved to large degree. A common camera obtains a face image, whether a face appears is detected via a face detection algorithm, the face area is segmented on the basis of existence of the face, andthe images obtained by segmentation are preprocessed. The characteristics corresponding to different models are extracted from the preprocessed images, and the processed, and a cosine distance is usedto measure the similarity between characteristics of a person not identified and those of people registered in a database. The disadvantages that a present method is not high in accuracy and low in robustness to face environment change as illumination, expression, attitude and shielding are overcome, and the face identification accuracy can be improved effectively.

Description

technical field [0001] The invention relates to the technical field of face recognition, in particular to a face recognition method and system based on multi-model and multi-channel. Background technique [0002] With the rapid development of computer technology and artificial intelligence, and the increasing demand of people, traditional identity authentication technologies such as fingerprint recognition, iris recognition and voice recognition can no longer meet people's needs. Compared with traditional identity authentication technologies, face recognition has the advantages of high reliability and convenience. [0003] A reliable face recognition system is mainly composed of five modules: face acquisition, face detection, preprocessing, feature extraction, and classification recognition. Among them, feature extraction is the most critical and important step in the system. However, the face features extracted using a single existing feature extraction algorithm have low...

AI Technical Summary

Problems solved by technology

However, the face features extracted using a single existing feature extraction algorithm have low robustness and are very sensitive to changes in the face environment (illumination, expression, posture, occlusion)

It can be seen that in order to solve the problem that the accuracy of face recognition is limited by a single face feature extraction algorithm, it is currently necessary to integrate multiple feature extraction algorithms to improve the accuracy of face recognition.

Images