Facial expression representation and recognition based on texture augmentation and topographic masking

The variation of facial texture and surface due to the change of expression is an important cue for analyzing and modeling facial expressions. In this paper, we propose a new approach to represent the facial expression by using a so-called topographic feature. In order to capture the variation of facial surface structure, facial textures are processed by increasing the resolution. The topographical structure of human face is analyzed based on the resolution-enhanced textures. We investigate the relationship between the facial expression and its topographic features, and propose to represent the facial expression by the topographic labels. The detected topographic facial surface and the expressive regions reflect the status of facial skin movement. Based on the observation that the facial texture and its topographic features change along with facial expressions, we compare the disparity of these features between the neutral face and the expressive face to distinguish a number of universal expressions. The experiment demonstrates the feasibility of the proposed approach for facial expression representation and recognition.