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论文类型：会议论文

发表时间：2011-05-29

收录刊物：Scopus、CPCI-S、EI

卷号：6676

期号：PART 2

页面范围：296-304

关键字：Face recognition; Gabor wavelets; Marginal Fisher analysis; Manifold learning; Error correction SVM

摘要：In previous work, we proposed the Gabor manifold learning method for feature extraction in face recognition, which combines Gabor filtering with Marginal Fisher Analysis (MFA). and obtained better classification result than conventional subspace analysis methods. In this paper we propose an Enhanced Marginal Fisher Model (EMFM). to improve the performance by selecting eigenvalues in standard MFA procedure. and further combine Gabor filtering and EMFM as Gabor-based Enhanced Marginal Fisher Model (GEMFM) for feature extraction. The GEMFM method has better generalization ability for testing data, and therefore is more capable for the task of feature extraction in face recognition. Then, the GEMFM method is integrated with the error correction SVM classifier to form a new face recognition system. We performed comparative experiments of various face recognition approaches on the ORL, AR and FERET databases. Experimental results show the superiority of the GEMFM features and the new recognition system.