A Face Recognition Method and System Based on Sparse Representation and Mean Hash

Abstract

The invention discloses a face recognition method and system based on sparse representation and mean value hashing; it includes the following steps: performing preprocessing on face test samples and all face training samples: converting color face samples into grayscale images, and normalize the face test samples and all face training samples; use the sparse representation model to extract the sparsity features between samples, encode the face test samples into a linear combination of all face training samples, and calculate the face test The sparse coefficient matrix of the sample on all face training samples; extract the spatial structural features inside the face samples, calculate the mean hash feature of the face test samples and each face training sample; combine the sparse features between face samples It is fused with the spatial structural features inside the sample; the reconstruction error between the mean hash feature of the face test sample and the reconstructed face test sample is used to determine the final category of the face test sample.

Description

technical field [0001] The invention relates to a face recognition method and system based on sparse representation and mean hash. Background technique [0002] Face recognition is one of the core contents in the field of computer vision. It is the process of feature extraction, classification and recognition of face images. Authentication. Face recognition is a complex synthesis that integrates pattern recognition, computer vision, image processing and other technologies. It can be used in security inspection, security monitoring, criminal investigation tracking, identification and other fields, and has broad application prospects. However, due to the limitation of the number of face samples, intra-class differences, inter-class similarities, illumination changes and many other factors, how to effectively extract face features from small face sample data sets and efficiently implement face classification, Therefore, improving the robustness of face recognition and the acc...

AI Technical Summary

Problems solved by technology

However, the sparse representation algorithm only considers the sparsity between samples and ignores the spatial structure information inside the sample. When the face image is affected by strong illumination changes, the traditional sparse representation will reduce the robustness of face recognition. The recognition accuracy will drop significantly

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