Face recognition using fusion of feature learning techniques

Abstract

A method for face recognition system for both challenging frontal and profile faces is proposed in this paper. The proposed system consists of face pre-processing, feature extraction and classification components. During pre-processing, a region-of-interest for face region is extracted based on facial landmark points, obtained by a Tree Structured Part Model. During feature extraction, Scale Invariant Feature Transform descriptors are computed from patches over detected face region. These descriptors undergo to different feature learning techniques to obtain different feature representations for the input image. The performance of these feature representations are obtained using multi-class linear Support Vector Machine classifier during classification. Finally, the scores from different feature learning techniques are fused to take the decision to recognize the subjects. Extensive experimental results have been demonstrated to show the effectiveness of the proposed face recognition system. The comparison with the exiting state-of-art methods for ORL, IITK, CVL, AR, CASIA-Face-V5, FERET and CAS-PEAL face databases, show the superiority of the proposed system.