Face identification method based on multiscale weber local descriptor and kernel group sparse representation

Abstract

The invention discloses a face identification method based on multiscale weber local descriptor and kernel group sparse representation. The face identification method comprises the following steps: firstly normalizing the size of face images and smoothing the images by utilizing a gaussian filter; extracting differential excitation ingredients of the multiscale weber local descriptor of the images and extracting direction information by utilizing an Sobel operator; extracting the multiscale weber local descriptor of the face images according to the multiscale differential excitation and the direction information and mapping the multiscale weber local descriptor to a kernel space by utilizing a histogram intersection kernel; then with a kernel matrix obtained by a training sample as a sparse dictionary, calculating group sparse representation coefficients of a kernel vector obtained by a test sample; and finally reconstructing a multiscale weber local descriptor vector of the test sample according to the group sparse representation coefficients and distinguishing the test sample by utilizing the minimum reconstruction error. According to the face identification method, the multiscale weber local descriptor and the kernel group sparse representation algorithm are fused for face identification, and the identification accuracy rate is greatly improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a face recognition method based on multi-scale Weber Local Descriptor (WLD, Weber Local Descriptor) and Kernel Group Sparse Representation (KGSR, Kernel Group Sparse Representation). Background technique [0002] Face recognition refers to the biometric identification technology that uses facial feature information for identity identification. It has the advantages of non-contact acquisition, concealed operation, convenience and speed, powerful post-event tracking ability, strong interactivity and low image acquisition cost. It is widely used In video surveillance, criminal detection, public security, human-computer interaction and other fields. Under controllable conditions, existing face recognition methods generally have good recognition performance. However, with changes in lighting, facial posture, expression, occlusion and other factors, the recognition performanc...

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Problems solved by technology

However, with changes in factors such as illumination, facial posture, expression, occlusion, etc., the recognition performance will decrease significantly.

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