Knowledge Conditioned Variational Learning for One-Class Facial Expression Recognition

The openness of application scenarios and the difficulties of data collection make it impossible to prepare all kinds of expressions for training. Hence, detecting expression absent during the training (called alien expression) is important to enhance the robustness of the recognition system. So in this paper, we propose a facial expression recognition (FER) model, named OneExpressNet, to quantify the probability that a test expression sample belongs to the distribution of training data. The proposed model is based on variational auto-encoder and enjoys several merits. First, different from conventional one class classification protocol, OneExpressNet transfers the useful knowledge from the related domain as a constraint condition of the target distribution. By doing so, OneExpressNet will pay more attention to the descriptive region for FER. Second, features from both source and target tasks will aggregate after constructing a skip connection between the encoder and decoder. Finally, to further separate alien expression from training expression, empirical compact variation loss is jointly optimized, so that training expression will concentrate on the compact manifold of feature space. The experimental results show that our method can achieve state-of-the-art results in one class facial expression recognition on small-scale lab-controlled datasets including CFEE and KDEF, and large-scale in-the-wild datasets including RAF-DB and ExpW.