Generative Neutral Features-Disentangled Learning for Facial Expression Recognition

Facial expression recognition (FER) plays a critical role in human-computer interaction and affective computing. Traditional FER methods typically rely on comparing the difference between an examined facial expression and a neutral face of the same person to extract the motion of facial features and filter out expression-irrelevant information. With the extensive use of deep learning, the performance of FER has been further improved. However, existing deep learning-based methods rarely utilize neutral faces. To address this gap, we propose a novel deep learning-based FER method called Generative Neutral Features-Disentangled Learning (GNDL), which draws inspiration from the facial feature manifold. Our approach integrates a neutral feature generator (NFG) that generates neutral features in scenarios where the neutral face of the same subject is not available. The NFG uses fine-grained features from examined images as input and produces corresponding neutral features with the same identity. We train the NFG using a neutral feature reconstruction loss to ensure that the generative neutral features are consistent with the actual neutral features. We then disentangle the generative neutral features from the examined features to remove disturbance features and generate an expression deviation embedding for classification. Extensitive experimental results on three popular databases (CK+, Oulu-CASIA, and MMI) demonstrate that our proposed GNDL method outperforms state-of-the-art FER methods.