Full-Space LDA With Evolutionary Selection for Face Recognition

Authors:
Xin Li;Bin Li;Hong Chen;Xianji Wang;Zhengquan Zhuang
Affiliations:
MOE-Microsoft Key Laboratory of Multimedia Computing and Communication, University of Science and Technology of China, 230027 HeFei, Email: simonlee@mail.ustc.edu.cn, China;MOE-Microsoft Key Laboratory of Multimedia Computing and Communication, University of Science and Technology of China, 230027 HeFei, Email: simonlee@mail.ustc.edu.cn, China;MOE-Microsoft Key Laboratory of Multimedia Computing and Communication, University of Science and Technology of China, 230027 HeFei, Email: simonlee@mail.ustc.edu.cn, China;MOE-Microsoft Key Laboratory of Multimedia Computing and Communication, University of Science and Technology of China, 230027 HeFei, Email: simonlee@mail.ustc.edu.cn, China;MOE-Microsoft Key Laboratory of Multimedia Computing and Communication, University of Science and Technology of China, 230027 HeFei, Email: simonlee@mail.ustc.edu.cn, China
Venue:
Computational Intelligence and Security
Year:
2007

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Abstract

Linear Discriminant Analysis (LDA) is a popular feature extraction technique for face recognition. However, it often suffers from the small sample size problem when dealing with the high dimensional face data. Some approaches have been proposed to overcome this problem, but they usually utilize all eigenvectors of null or range subspaces of within-class scatter matrix(Sw). However, experimental results testified that not all the eigenvectors in the full space of are positive to the classification performance, some of which might be negative. As far as we know, there have been no effective ways to determine which eigenvectors in full space should be adopted. This paper proposes a new method EDA+Full-space LDA, which takes full advantage of the discriminative information of the null and range subspaces of by selecting an optimal subset of eignvectors. An Estimation of Distribution Algorithm (EDA) is used to pursuit a subset of eigenvectors with significant discriminative information in full space of . EDA+Full-space LDA is tested on ORL face image database. Experimental results show that our method outperforms other LDA methods.