Robust Face Recognition Under Pose, Illumination and Expression Variations Using L1 Graph Method

As one of the most successful applications of image analysis and understanding face recognition has recently received significant attention, especially during the past several years. Automated face recognition (AFR) has received increased attention in recent years. The system aims in solving the problems occurred in face recognition, and Face recognition is one of the most intensively studied topics in computer vision and pattern recognition, but few are focused on how to robustly recognize faces with expressions under the restriction of one single training sample per class. A L1 graph algorithm, which combines the advantages of the unambiguous correspondence of feature point labeling and the flexible representation of illumination complexities, pose variations and expression variations computation, our proposed approach has been developed for face recognition from expression invariant, pose invariant and illumination invariant face images. In this paper, we propose an integrated face recognition system that is robust against facial expressions by combining information from the computed intrapersonal and the synthesized face image in a probabilistic framework. This method show that the proposed system improves the accuracy of face recognition from expressional, illuminations variant and pose variant face images can be accurately treated with accurate results.