Boosted multi-resolution spatiotemporal descriptors for facial expression recognition

Recently, a spatiotemporal local binary pattern operator from three orthogonal planes (LBP-TOP) was proposed for describing and recognizing dynamic textures and applied to facial expression recognition. In this paper, we extend the LBP-TOP features to multi-resolution spatiotemporal space and use them for describing facial expressions. AdaBoost is utilized to learn the principal appearance and motion, for selecting the most important expression-related features for all the classes, or between every pair of expressions. Finally, a support vector machine (SVM) classifier is applied to the selected features for final recognition.