Dual-Branch Residual Disentangled Adversarial Learning Network for Facial Expression Recognition

The facial expression recognition is very important for human-computer interaction. Therefore, a large number of researchers are focusing on this topic research and have acquired many valuable research achievements. However, there still exist many problems that need to be solved for practical applications, such as the impact of identity and appearance differences, posture change etc. In this work, a dual-branch residual disentangled adversarial learning network is proposed to learn more accurate expression features by disentangling the non-expression features from basic features through a novel combinatorial loss function. In the proposed method, dual-branch network structure is designed, one branch with a D-Net module is utilized to explore non-expression features and another branch just uses subtraction operation to obtain expression features. Based on the above network structure, a novel loss function is constructed to guide the two branches to learn different type features, which contains expression recognition loss, adversarial loss and cosine similarity loss. The main highlight of this work is that the proposed method could achieve the disentanglement of expression features and non-expression features just based on a low-complexity network and expression datasets without other auxiliary data. Finally, abundant experimental results on multiple expression datasets have confirmed the proposed method could obtain better expression recognition results than other state-of-the-art methods.