Expression Recognition

In this paper, we propose a novel facial expression recognition method enhanced by thermal images. Our method leverages thermal images to construct better visible feature representation and classifiers during training through adversarial learning and similarity constraints. Specifically, we learn two deep neural networks for expression classification from visible and thermal images.

In this paper, we introduce a novel dynamic model, termed as interval temporal restricted Boltzmann machine(IT-RBM), to jointly capture global spatial patterns and complex temporal patterns embedded in posed expressions and spontaneous expressions respectively for distinguishing posed and spontaneous expressions. The proposed IT-RBM is a three-layer hierarchical probabilistic graphical model.

In this paper, we propose employing the LRBN to effectively capture the high-order and global dependencies among facial geometric features

Although their constructed representation reflects thermal infrared images’ supplementary role for visible images, it has no direct relationship to target expression labels. Furthermore, the hand-craft visible and thermal features may not thoroughly reflect the expression patterns embedded in images. Therefore, in this paper, we propose a new deep two-view approach to learn features from both visible and thermal images and leverage the commonality among visible and thermal images for expression recognition.