Hierarchical Bayesian Theme Models for Multipose Facial Expression Recognition

As an essential way of human emotional behavior understanding, facial expression recognition (FER) has attracted a great deal of attention in multimedia research. Most of studies are conducted in a “lab-controlled” environment, and their real-world performance degenerates greatly due to factors such as head pose variations. In this paper, we propose a pose-based hierarchical Bayesian theme model to address challenging issues in multipose FER. Local appearance features and global geometry information are combined in our model to learn an intermediate face representation before recognizing expressions. By sharing a pool of features with various poses, our model provides a unified solution for multipose FER, bypassing the separate training and parameter tuning for each pose, and thus is scalable to a large number of poses. Experiments on both benchmark facial expression databases and Internet images show the superior/highly competitive performance of our system when compared with the current state of the art.