Face recognition system based on a manifold learning subspace algorithm

Abstract

The invention discloses a face recognition system based on a manifold learning subspace algorithm. The system comprises a data analysis and processing center, the data analysis and processing center comprises a data processing module, an image comparison module and a data receiving module, the data processing module comprises an irrelevant linear discriminant embedding module and an orthogonal unsupervised discriminant mapping module, and a server is arranged at the output end of the data analysis and processing center. According to the invention, the filter is arranged, and the filter is setto a Log-Gabor filter and can transfer a function as a high function on a logarithmic frequency scale. the frequency response of the original image can be reflected more truly; the filter can have nodirect component, the bandwidth is not limited, the filter with any bandwidth and zero DC component can be constructed, the natural image can be better represented, the filter works to expand the feature dimension of the video image, a data processing center can conveniently carry out dimension reduction calculation on the video image, and a more accurate result is obtained.

Description

technical field [0001] The invention relates to the field of face recognition, in particular to a face recognition system based on a manifold learning subspace algorithm. Background technique [0002] Traditional face recognition algorithms are generally based on image grayscale matrix operations, and image grayscale matrices are not enough to fully describe facial feature information, and there are many high-level information redundancy, which is not conducive to traditional face recognition algorithms. Dealing with the problem of nonlinear structures such as faces, taking the neighborhood discriminant embedding algorithm as the research object, extending the algorithm to the nonlinear field, and studying the application of the kernelized nonlinear manifold learning subspace algorithm in face recognition, because The traditional manifold learning subspace algorithm generally performs low-dimensional projection by solving the eigenvectors corresponding to the eigenvalues ​​o...

AI Technical Summary

Problems solved by technology

[0002] Traditional face recognition algorithms are generally based on image grayscale matrix operations, and image grayscale matrices are not enough to fully describe facial feature information, and there are many high-level information redundancy, which is not conducive to traditional face recognition algorithms. Dealing with the problem of nonlinear structures such as faces, taking the neighborhood discriminant embedding algorithm as the research object, extending the algorithm to the nonlinear field, and studying the application of the kernelized nonlinear manifold learning subspace algorithm in face recognition, because The traditional manifold learning subspace algorithm generally performs low-dimensional projection by solving the eigenvectors corresponding to the eigenvalues ​​of an asymmetric eigenequation, resulting in more information redundancy between the eigenvectors. Therefore, a manifold-based It is necessary to learn the face recognition system of subspace algorithm to solve the above problems

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