LBP (Local Binary Pattern) face recognition method for eliminating illumination evenness

Abstract

The invention discloses a LBP (Local Binary Pattern) face recognition method for eliminating illumination evenness, and solves a problem that a LBP face recognition rate is affected due to the illumination evenness. The invention adopts a technical scheme that a face grey level picture I is changed to a Log domain to obtain a face picture R with illumination invariability information by separation; the face picture I is subjected to threshold value segmentation to obtain a picture M marked with an illumination area and a shadow area; according to the picture M, combining the picture R and the picture I into one new picture N without the illumination area and the shadow area; extracting the LBP textural feature vectors of each subblock of the picture N; and finally, connecting the LBP textural feature vectors of each subblock in series in sequence to obtain a feature vector, wherein the vector is the LBP feature vector which is finally extracted for eliminating illumination influence. The influence on the face recognition by illumination is greatly reduced, the illumination robustness of the face recognition is effectively improved, and face recognition accuracy is improved.

Description

technical field [0001] The invention belongs to the technical fields of digital image processing and pattern recognition, and in particular relates to an LBP face recognition method for eliminating uneven illumination. Background technique [0002] Biometrics is a technology that uses human biometrics to identify an identity. The biometric characteristics studied by biometric technology include face, fingerprint, palm print, iris, retina and so on. Due to the uniqueness of human body characteristics, these human biological keys are difficult to be copied, stolen or lost, so biometric technology is more reliable and convenient than traditional identification methods. Face recognition is the fastest growing biometric technology in recent years. The main algorithms of face recognition include face recognition methods based on geometric features, face recognition methods based on template matching, face recognition methods based on sample learning, and face recognition methods...

AI Technical Summary

Problems solved by technology

In this way, although the influence of illumination on face recognition can be improved, the illumination component L(x,y) of the face image is discarded, and this part also carries the information of face features.

For face images with non-uniform illumination, generally part of the face is illuminated and part of the face is not illuminated. In this case, if the above method is used, illumination invariant features can be extracted for areas with illumination, but for areas without illumination The area will lose the information of the face features carried by L(x,y)

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