Individual feature-appearance for facial action recognition

Abstract

Automatic facial expression analysis is the most commonly studied aspect of behavior understanding and human-computer interface. Most facial expression recognition systems are implemented with general expression models. However, the same facial expression may vary differently across humans, this can be true even for the same person when the expression is displayed in different contexts. These factors present a significant challenge for recognition. To cope with this problem, we present in this paper a personalized facial action recognition framework that we wish to use in a clinical setting with familiar faces; in this case a high accuracy level is required. The graph fitting method that we are using offers a constrained tracking approach on both shape (using procrustes transformation) and appearance (using weighted Gabor wavelet similarity measure). The tracking process is based on a modified Gabor-phase based disparity estimation technique. Experimental results show that the facial feature points can be tracked with sufficient precision leading to a high facial expression recognition performance. © 2011 Springer-Verlag.