Multi-pose face recognition method based on MB-LBP features and face energy diagram

Abstract

The invention provides a multi-pose face recognition method based on MB-LBP features and a face energy diagram. According to the method, a multi-pose face image training library is established, and after size normalization processing is carried out on face images, a face mean value energy diagram and a variance energy diagram of the training library are established; MB-LBP feature extracting is carried out on the obtained face mean value energy diagram and the variance energy diagram, and the MB-LBP features are stored as matching library information; when face detection is carried out, the face images are detected and face regions are extracted, and size normalization processing is carried out on the images of the face regions to obtain standard face images; MB-LBP feature extracting is carried out on the standard face images; finally, classification and recognition of multi-pose faces are completed by adopting a nearest neighbor classifier. According to the multi-pose face recognition method based on the MB-LBP features and the face energy diagram, the intrinsic macroscopic features of the multi-pose faces can be maintained well, the microstructure and the macrostructure of a face image mode are reserved, influences brought by single pixel noise can be removed, needed storage space is small, and the method has an excellent recognition rate and recognition speed.

Description

technical field [0001] The invention belongs to the technical field of biological feature identification, and in particular relates to a multi-pose face recognition method based on MB-LBP features and a face energy map. Background technique [0002] Compared with other biometric identification methods such as fingerprints and irises, automatic face recognition technology has special advantages such as convenient collection and non-intrusiveness, so it has very broad application prospects and economic value. If face recognition is divided by pose, it can be divided into forward-looking face recognition and multi-pose face recognition. Among them, the technology of forward-looking face recognition is relatively mature. However, the multi-pose face recognition method still has many technical problems such as large storage capacity, complex calculation, and low recognition rate. The lag in research on multi-pose face recognition has become one of the main obstacles restricting...

AI Technical Summary

Problems solved by technology

Although the MB-LBP feature extraction method has achieved good results in texture analysis and face recognition application experiments, under the influence of complex factors such as severe illumination changes, extreme changes in imaging conditions, posture, expression, age, etc., MB-LBP The representation ability and classification ability of features are also limited, and the recognition performance drops sharply

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