Gradient feature matching for expression invariant face recognition using single reference image

Automatic recognition of human faces irrespective of the expression variations is a challenging problem. In this paper, we propose a novel method for face recognition based on `edge-strings´. Experimental studies on face perception have shown the significance of edge features in visual perception and learning. In the proposed technique, the edges of a face are identified, and a feature string is created from edge pixels. This forms a symbolic descriptor corresponding to the edge image referred to as `edge-string´. The `edge-strings´ are then compared using the Smith-Waterman algorithm to match them. The class corresponding to each image is identified based on the number of string primitives that match. Local string alignment algorithm is more robust to noise than global alignment algorithm; it gives better performance even if the input image is noisy. In addition, this method needs only a single training image per class. The proposed technique is a good solution for expression invariant face recognition. The effectiveness of the proposed method is compared with state-of-the-art algorithms on the Yale Face database, the Japanese Female Face Expression database (JAFFE) and CMU AMP Face EXpression database.