A Novel Pose Tolerant Face Recognition Approach

Face recognition is biometric pattern recognition, which is more acceptable and convenient for users compared with other biometric recognition traits. Among many problems in face recognition system, pose problem is still considered as one of the major problem still unsolved in satisfactory level. This paper proposes a novel pose tolerant face recognition approach. Proposed system´s contribution is a novel and efficient approach that includes feature extraction, pose transformation learning and recognition stages. In first stage, 2D PCA is used as robust feature extraction technique. The linear regression is used as efficient and accurate transformation learning technique to create frontal face image from a different posed face images in the second stage. In last stage, Mahalanobis distance is used for recognition. Experiments on FERET and FEI face databases demonstrate higher performance in comparison with traditional systems.