Face Recognition Based on Gabor-Enhanced Manifold Learning and SVM

Recently proposed Marginal Fisher Analysis (MFA), as one of the manifold learning methods, has obtained better classification results than the conventional subspace analysis methods and other manifold learning algorithms such as ISOMAP and LLE, because of its ability to find the intrinsic structure of data space and its nature of supervised learning as well. In this paper, we first propose a Gabor-based Marginal Fisher Analysis (GMFA) approach for face feature extraction, which combines MFA with Gabor filtering. The GMFA method, which is robust to variations of illumination and facial expression, applies the MFA to augmented Gabor feature vectors derived from the Gabor wavelet representation of face images. Then, the GMFA method is integrated with the Error Correction SVM classifier to form a novel face recognition system. We performed comparative experiments of various face recognition approaches on ORL database and FERET database. Experimental results show superiority of the GMFA features and the new recognition system presented in the paper.