Face identification method and face identification system based on fusion of multiple classifiers

Abstract

The invention relates to a face identification method and a face identification system based on fusion of multiple classifiers. The method comprises following steps that a second classifer acquires a left-handed face image with a pose range of [-90, -15], a third classifier acquires a front face image with a pose range of [-15, +15], a fourth classifier acquires a right-handed face image with a pose range of [+15, +90], and comparing faces in the face images; fusing the face image with the comparison results of the same compared person in a pose module database to acquire an identification result, the identified relevant information of the compared person is displayed or the left-handed face image, the front face image and the right-handed face image are stored into the pose module database. Due to the adoption of the method and the system, a first comparison result, a second comparison result and a third comparison result can be effectively fused, the variation situation of the face poses in an application environment can be effectively processed in real time, and the accuracy and the robustness for identifying the multi-pose face can be improved.

Description

technical field [0001] The invention relates to a face recognition method and system based on multi-classifier fusion. Background technique [0002] The ability of the classifier to distinguish all sample features has a fatal impact on the performance of the face recognition system. In the actual monitoring system application process, the data source is mostly based on the dynamic video stream collected by the camera. The collected face Images often have the problem of large pose randomness, and traditional face recognition methods or systems usually use only one classifier for recognition, which makes it difficult to achieve a high recognition rate only by relying on one classifier, thus limiting The application of face recognition system in surveillance. Therefore, how to consider the impact of posture changes on face recognition and improve the accuracy of face recognition is an urgent problem to be solved. Contents of the invention [0003] The purpose of the present...

AI Technical Summary

Problems solved by technology

[0002] The ability of the classifier to distinguish all sample features has a fatal impact on the performance of the face recognition system. In the actual monitoring system application process, the data source is mostly based on the dynamic video stream collected by the camera. The collected face Images often have the problem of large pose randomness, and traditional face recognition methods or systems usually use only one classifier for recognition, which makes it difficult to achieve a high recognition rate only by relying on one classifier, thus limiting Problems in the application of face recognition system in surveillance

Images