Spatiotemporal Features for Effective Facial Expression Recognition

We consider two novel representations and feature extraction schemes for automatic recognition of emotion related facial expressions. In one scheme facial landmark points are tracked over successive video frames using an effective detector and tracker to extract landmark trajectories. Features are extracted from landmark trajectories using Independent Component Analysis (ICA) method. In the alternative scheme, the evolution of the emotion expression on the face is captured by stacking normalized and aligned faces into a spatiotemporal face cube. Emotion descriptors are then 3D Discrete Cosine Transform (DCT) features from this prism or DCT & ICA features. Several classifier configurations are used and their performance determined in detecting the 6 basic emotions. Decision fusion applied to classifiers improved the recognition performance of best classifier by 9 percentage points. The proposed method was evaluated user independently on the Cohn-Kanade facial expression database and a state-of-the-art 95.34 % recognition performance is achieved.