Human face recognition method based on image reconstruction and Hash algorithm

Abstract

The invention discloses a human face recognition method based on image reconstruction and a Hash algorithm. An image reconstruction model is adopted, a dimension variable ap is used, shields and covers in a human face image are effectively eliminated, and the problems of uneven illumination and image drifting in the human face image are resolved. A Hash matrix is introduced, a high-dimensional data matrix is mapped into a low-dimensional data space, and the calculation amount of the algorithm is largely reduced. Under the condition of guaranteeing recognition accuracy, the problem of multiple inputs in the human face recognition process is effectively resolved, and the recognition efficiency of the algorithm is improved.

Description

technical field [0001] The invention belongs to the technical field of face recognition, in particular to a face recognition method based on image reconstruction and hash algorithm. Background technique [0002] Face recognition is a popular research topic in the field of computer vision. It integrates computer image processing technology and biostatistics principles, uses computer image processing technology to extract portrait feature points from videos, and uses biostatistics principles to analyze and establish Mathematical models have broad prospects for development. 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] Recently, the face recognition algorithm (SRC) based on sparse representation proposed by John Wright has attracted more and more attention from researchers. Under the constraints of sparse conditions, SR...

AI Technical Summary

Problems solved by technology

Although these algorithms can achieve better recognition results under certain circumstances, there are still some defects: SRC is mainly for single-input face recognition algorithms, and the face images taken by the same individual under different conditions can only be one One determines the identity of the individual through the detection and recognition of the algorithm, which greatly increases the time of recognition, and more importantly, ignores the correlation between these face images

Although some good results can be obtained, when the illumination and occlusion of the face image are serious, the recognition rate of this method will decrease accordingly.

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