Get the FACS fast: Automated FACS face analysis benefits from the addition of velocity

Much progress has been made in automated facial image analysis, yet current approaches still lag behind what is possible using manual labeling of facial actions. While many factors may contribute, a key one may be the limited attention to dynamics of facial action. Most approaches classify frames in terms of either displacement from a neutral, mean face or, less frequently, displacement between successive frames (i.e. velocity). In the current paper, we evaluated the hypothesis that attention to dynamics can boost recognition rates. Using the well-known Cohn-Kanade database and support vector machines, adding velocity and acceleration decreased the number of incorrectly classified results by 14.2% and 11.2%, respectively. Average classification accuracy for the displacement and velocity classifier system across all classifiers was 90.2%. Findings were replicated using linear discriminant analysis, and found a mean decrease of 16.4% in incorrect classifications across classifiers. These findings suggest that information about the dynamics of a movement, that is, the velocity and to a lesser extent the acceleration of a change, can helpfully inform classification of facial expressions.