Rotated face recognition by manifold learning with auto-associative neural network

The performance of face recognition is easily affected by appearance variation by face rotation. The proposed method in this research recognizes who is a subject in the query image in which a face is captured from an arbitrary direction. The proposed method employs an auto-associative neural network for learning a manifold which represents principal variation of facial appearance in feature space due to face rotation. Our comparison where four conditions of selecting training samples for manifold learning were adopted implied that rotated third parson faces and its reference frontal face can be applicable for the manifold learning. The results in evaluation experiments with SCface database showed that the highest recognition accuracy at RANK10 is 77.5 %.