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.