Fusion of Facial Features using DS Theory for Face Recognition

This paper proposes an efficient face recognition system that uses salient facial features extracted from eyes, mouth and nose of a face image. SIFT descriptor is used to determine all facial keypoints from each landmark. These feature points are considered to obtain a relaxation graph. Given two relaxations graphs from a pair of faces, matching scores between two corresponding feature points has been determined with the help of iterative graph relaxation cycles. Dempster-Shafer decision theory is used to fuse all these matching scores obtained from each pair of facial landmarks for taking the final decision. The proposed technique has been tested against the three databases, namely, FERET, ORL and IITK face databases. The experimental results exhibit efficacy of the proposed face recognition system.