Intensity Estimation of Spontaneous Facial Action Units Based on Their Sparsity Properties.

Automatic measurement of spontaneous facial action units (AUs) defined by the facial action coding system (FACS) is a challenging problem. The recent FACS user manual defines 33 AUs to describe different facial activities and expressions. In spontaneous facial expressions, a subset of AUs are often occurred or activated at a time. Given this fact that AUs occurred sparsely over time, we propose a novel method to detect the absence and presence of AUs and estimate their intensity levels via sparse representation (SR). We use the robust principal component analysis to decompose expression from facial identity and then estimate the intensity of multiple AUs jointly using a regression model formulated based on dictionary learning and SR. Our experiments on Denver intensity of spontaneous facial action and UNBC-McMaster shoulder pain expression archive databases show that our method is a promising approach for measurement of spontaneous facial AUs.