A Face Recognition Method Based on Low-rank Block Sparse Representation

Abstract

The invention discloses a low-rank partitioning sparse representation human face identifying method, which adopts low-rank matrix decomposition, introduces a reference item, adopts a DCT (discrete cosine transform) algorithm to realize the normalization of images and effectively solves the problem of uneven lighting in a human face image. At a classifying stage, a clustering thought is used, and the identifying speed is effectively improved. The algorithm is used on a standard human face database to perform multiple times of tests, and the test result shows that compared with the existing human face identifying method, the low-rank partitioning sparse representation human dace identifying method has the advantage that the identifying accuracy and computing efficiency of the algorithm are all consistently improved. The precision and stability on human face identifying are improved under the complex conditions, such as shielding, disguising and illumination varying, of the human face image.

Description

technical field [0001] The invention discloses a face recognition method with low-rank block sparse representation, and relates to the fields of image processing and pattern recognition. Background technique [0002] Face recognition is a popular research topic in the field of computer vision. It widely uses feature analysis algorithms, integrates computer image processing technology and biostatistics principles, and uses computer image processing technology to extract portrait feature points from videos. The principle of biostatistics is used to analyze and establish mathematical models, which has broad development prospects. For a robust face recognition algorithm, it is necessary to effectively deal with various challenges in face recognition such as face occlusion, camouflage, illumination changes, and image drift. [0003] Sparse coding is an emerging face recognition algorithm. It regards the face image to be recognized as a linear combination of all training images ...

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is: face recognition methods in the prior art cannot effectively deal with occlusion, camouflage and illumination changes in face images at the same time. Recognition method, in order to improve the accuracy and robustness of face recognition in complex situations such as occlusion, camouflage, and illumination changes in face images

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