A multi-pose face recognition method based on mb‑lbp features and face energy maps

Abstract

The invention provides a multi-pose face recognition method based on MB-LBP features and face energy maps. In the present invention, by establishing a multi-pose face image training library, after the face image is subjected to size normalization processing, the face mean energy map and variance energy map of the training library are constructed; then the obtained face mean energy map and variance energy MB‑LBP feature extraction is performed on the image and stored as matching library information; when face detection is performed, the face image is detected and the face area is extracted, and the size of the face area image is normalized to obtain a standard face image; perform MB‑LBP feature extraction on the standard face image; finally use the nearest neighbor classifier to complete the classification and recognition of multi-pose faces. The present invention can better retain the inherent appearance features of multi-pose faces, and retains the microstructure and macrostructure of face image patterns, can remove the influence of single pixel noise, requires less storage space, and has 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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