Pose-Invariant Face Recognition Using Deformation Analysis

Over the last decade or so, face recognition has become a popular area of research in computer vision and one of the most successful applications of image analysis and understanding. In addition, recognition of faces under varied poses has been a challenging area of research due to the complexity of pose dispersion in feature space. This paper presents a novel and robust pose-invariant face recognition method. In this approach, first, the facial region is detected using the TSL color model. The direction of face or pose is estimated using facial features and the estimated pose vector is decomposed into X-Y-Z axes. Second, the input face is mapped by a deformable template using these vectors and the 3D CANDIDE face model. Finally, the mapped face is transformed to the frontal face which appropriates for face recognition by the estimated pose vector. Through the experiments, we come to validate the application of face detection model and the method for estimating facial poses. Moreover, the tests show that recognition rate is greatly boosted through the normalization of the poses.