Dynamic Weighting of Facial Features for Automatic Pose-Invariant Face Recognition
This paper proposes an automatic pose-invariant face recognition system. In our approach, we consider the texture information around the facial features to compute the similarity measure between the probe and gallery images. The weight of each facial feature is dynamically estimated based on its robustness to the pose of the captured image. An approach to extract the 9 facial features used to initialize the Active shape model is proposed. The approach is not dependent on the texture around the facial feature only but incorporates the information obtained about the facial feature relations. Our face recognition system is tested on common datasets in pose evaluation CMU-PIE and FERET. The results show out-performance of the state of the art automatic face recognition systems.