Generalized Discriminant Local Median Preserving Projections (GDLMPP) for Face Recognition
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To solve the problem of the singularity of the within-class scatter matrix in discriminant local median preserving projections (DLMPP) in the case of small sample size problem, an algorithm named generalized local median preserving projection (GDLMPP) is proposed. To solve the small size problem, GDLMPP firstly transforms the samples into a lower dimensional space equivalently, and then the optimal projection matrix can be solved. The theoretical analysis shows that GDLMPP is equivalent to DLMPP when the within-class scatter matrix is non-singular. Finally, we conduct extensive experiments to prove that the proposed algorithm can provide a better representation and achieve higher face recognition rates than previous approaches such as LPP, LDA and DLMPP on the ORL, Yale and AR face databases.
KeywordsFace recognition Feature extraction Small sample size problem Discriminant local median preserving projections
This work is partially supported by National Key R&D Program Grant No. 2017YFC0804002, the National Science Foundation of China under Grant Nos. 61462064, 6177227, 61362031, 61463008, 61403188, 61503195, 61603192, and the China Postdoctoral Science Foundation under Grant No. 2016M600674, the Natural Science Fund of Jiangsu Province under Grant BK20161580, BK20171494 and China’s Aviation Science (No. 20145556011).
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