A joint dictionary learning and regression model for intensity estimation of facial AUs

Automated intensity estimation of spontaneous Facial Action Units (AUs) defined by Facial Action Coding System (FACS) is a relatively new and challenging problem. This paper presents a joint supervised dictionary learning (SDL) and regression model for solving this problem. The model is casted as an optimization function consisting of two terms. The first term in the optimization concerns representing the facial images in a sparse domain using dictionary learning whereas the second term concerns estimating AU intensities using a linear regression model in the sparse domain. The regression model is designed in a way that considers disagreement between raters by a constant biasing factor in measuring the AU intensity values. Furthermore, since the intensity of facial AU is a non-negative value (i.e., the intensity values are between 0 and 5), we impose a non-negative constraint on the estimated intensities by restricting the search space for the dictionary learning and the regression function. Our experimental results on DISFA and FERA2015 databases show that this approach is very promising for automated measurement of spontaneous facial AUs.