Bi-Center Loss for Compound Facial Expression Recognition

Compound facial expressions involve combinations of basic emotions, posing challenges to automatic facial expression recognition research. The focus of existing studies in facial expression recognition remains primarily on classifying basic or single expressions, which limits its application to compound facial expression recognition. Moreover, some compound facial expression recognition methods rely on laboratory-controlled expression data, narrowing their generalizability to real-world scenarios. In this work, we carry out the task of compound facial expression recognition in unconstrained environments. To this end, we devise a new loss function, specifically for compound facial expression recognition, called bi-center loss, which is built upon center loss. Unlike center loss that considers all categories, bi-center loss enables deep neural networks to learn compound emotion features by leveraging basic emotion centers. Additionally, we introduce a basic-center regularization term, based on the variance among the basic centers, to ensure appropriate discriminative capabilities of the learned features. Experiments conducted on unconstrained compound expression datasets demonstrate the effectiveness of the proposed method over the baselines, achieving state-of-the-art performance in compound facial expression recognition.