Learning Bases of Activity for Facial Expression Recognition.

The extraction of descriptive features from sequences of faces is a fundamental problem in facial expression analysis. Facial expressions are represented by psychologists as a combination of elementary movements known as action units: each movement is localised and its intensity is specified with a score that is small when the movement is subtle and large when the movement is pronounced. Inspired by this approach, we propose a novel data-driven feature extraction framework that represents facial expression variations as a linear combination of localised basis functions, whose coefficients are proportional to movement intensity. We show that the linear basis functions required by this framework can be obtained by training a sparse linear model with Gabor phase shifts computed from facial videos. The proposed framework addresses generalisation issues that are not addressed by existing learnt representations, and achieves, with the same learning parameters, state-of-the-art results in recognising both posed expressions and spontaneous micro-expressions. This performance is confirmed even when the data used to train the model differ from test data in terms of the intensity of facial movements and frame rate.