Face Recognition Based on Gabor Enhanced Marginal Fisher Model and Error Correction SVM

Abstract

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.