Human face recognition method based on multiple-feature space sparse classifiers

Abstract

The invention provides a human face recognition method based on multiple-feature space sparse classifiers. The human face recognition method includes the following steps that original training samples, namely X1....XN, are projected onto an Eigenface feature space, a Laplacianface feature space and a Gabor feature space respectively to form a sub-dictionary OE, a sub-dictionary OL and a sub-dictionary OG; the genetic algorithm is used for carrying out joint optimization and training on the three sub-dictionaries to obtain a sub-dictionary NE, a sub-dictionary NL and a sub-dictionary NG; the sub-dictionary NE, the sub-dictionary NL and the sub-dictionary NG are used for training the sparse classifier SRCs. Each sparse classifier carries out sparse representation on a sample to be tested and obtains residual errors corresponding to the ith training sample, wherein the residual errors are RiE, RiL and RiG respectively and then, the mean value of the residual errors corresponding to the ith training sample are calculated. A category corresponding to the minimum value of the residual error mean value E[Ri] is the category which the human face sample to be tested belongs to. According to the human face recognition method based on the multiple-feature space sparse classifiers, the adopted dictionary training method can select a sample with the best separating capacity from each sub-dictionary, so that the human face recognition accuracy based on the sparse classifiers with the dictionaries is improved.

Description

technical field [0001] The invention belongs to the technical field of image recognition and relates to a face recognition method. Background technique [0002] Face recognition has always been a popular subject in the field of computer vision. Wright et al. proposed to use the sparse classifier (SRC) based on compressed sensing theory to recognize faces, and achieved good results. However, the algorithm directly uses the training picture as a sparse representation of the L1 norm constraint on the detection picture, which obviously cannot fully represent the characteristics of the face picture to be tested, and the high number of atoms in the dictionary increases the complexity of the encoding. [0003] Therefore, how to learn the optimal dictionary from the original training samples has become a hot research direction. There are currently many dictionary learning algorithms for face recognition: [0004] 1. Metaface, KSVD, etc. all learn the original training samples unif...

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Problems solved by technology

[0006] The two types of dictionary learning methods mentioned above are all learning the original training pictures in a single feature space, which cannot make full use of the characteristics of the original training samples, and the generalization ability is not good.

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