Facial expression recognition based on shape and texture

In this paper, an efficient method for human facial expression recognition is presented. We first propose a representation model for facial expressions, namely the spatially maximum occurrence model (SMOM), which is based on the statistical characteristics of training facial images and has a powerful representation capability. Then the elastic shape-texture matching (ESTM) algorithm is used to measure the similarity between images based on the shape and texture information. By combining SMOM and ESTM, the algorithm, namely SMOM-ESTM, can achieve a higher recognition performance level. The recognition rates of the SMOM-ESTM algorithm based on the AR database and the Yale database are 94.5% and 94.7%, respectively.