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Multiple-sparse-representation face recognition method for solving small sample size problem

A sparse representation, face recognition technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve problems such as complex cost

Inactive Publication Date: 2015-01-07
EAST CHINA JIAOTONG UNIVERSITY
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Problems solved by technology

The disadvantage of this method is that the solution of this kernel sparse coding model is more complicated and expensive than the classical sparse representation model

Method used

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  • Multiple-sparse-representation face recognition method for solving small sample size problem
  • Multiple-sparse-representation face recognition method for solving small sample size problem
  • Multiple-sparse-representation face recognition method for solving small sample size problem

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[0034] The present invention will be further described now in conjunction with accompanying drawing, see figure 1 , a multi-sparse representation classification method, including the following specific steps:

[0035] (1) Input sample 101 and produce virtual training sample 102; In this process, the face image is stored in matrix form, and the size of the matrix is ​​long and high all set to even numbers, to facilitate the follow-up mirror transformation operation. Two mirror operations are used to generate virtual training samples, and the specific process of one mirror transformation is as follows: record any image matrix as I, and its mirror image matrix as M, then M(i,j)=I(i,t-j+1 ), i=1, 2,..., s, j=1, 2,..., t, where s and t are the number of rows and columns of the image I, respectively.

[0036] (2) The feature extraction process includes three methods KPCA, KDA and KLPP, and their corresponding processes are 103, 104 and 105 respectively. In this process, face image...

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Abstract

Provided is a multiple-sparse-representation face recognition method for solving the small sample size problem. In the method, two modes are adopted to solve the small sample size problem during face recognition, one mode is that given original training samples produce 'virtual samples' so as to increase the number of the training samples, and the other mode is that three nonlinear feature extraction methods, namely a kernel principle component analysis method, a kernel discriminant analysis method and a kernel locality preserving projection algorithm method are adopted to extract features of the samples on the basis that the virtual samples are produced. Therefore, three feature modes are obtained, sparse-representation models are established for each feature mode. Three sparse-representation models are established for each sample, and finally classification is performed according to representation results. By means of the multiple-sparse-representation face recognition method, virtual faces are produced through mirror symmetry, and then norm L1 based multiple-sparse-representation models are established and classified. Compared with other classification methods, the multiple-sparse-representation face recognition method is good in robustness and classification effect and is especially suitable for a lot of classification occasions with high data dimensionality and few training samples.

Description

technical field [0001] The invention relates to a face recognition method with multi-sparse representation in the case of small samples, and belongs to the technical field of pattern recognition and machine learning. Background technique [0002] With the development of technologies such as computers, networks, and multimedia, people need to process more and more high-dimensional and complex data such as images and videos, and most of the processing of these data is classification or identification. In recent years, an important branch of image recognition, that is, biometric recognition, is in the ascendant, and it is a research hotspot in the field of pattern recognition. Compared with other biometric identification technologies such as fingerprint identification, face recognition has been widely concerned and used due to its ease of use. For example, after 9 / 11, the United States adopted face recognition systems in several airports. Both the 2008 Beijing Olympic Games an...

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Application Information

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IPC IPC(8): G06K9/66G06K9/46
CPCG06V40/168G06F18/24G06F18/214
Inventor 范自柱倪明康利攀
Owner EAST CHINA JIAOTONG UNIVERSITY
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