基于因果干预的无偏面部动作单元识别

Facial action unit (AU) recognition is a hot topic in the fields of computer vision and affective computing. AU recognition is a multi-label binary classification task, and currently faces challenges such as label imbalance. Most existing methods re-balance labels by adjusting the sampling rate and weights of AUs based on the correlations among AUs. However, these methods only shift the model’s prediction bias from high-frequency labels to low-frequency ones, and the bias is still unresolved. Fair treatment of each AU class, including the head and tail classes, is the key to achieve unbiased AU recognition. By introducing causal inference theory, we propose an unbiased AU recognition method CIU (Causal Intervention for Unbiased facial action unit recognition), which adjusts the empirical risks in both the imbalanced and balanced but invisible domains to achieve model unbiasedness. Extensive experiments demonstrate that our method outperforms state-of-the-art methods on BP4D and DISFA benchmarks, in which 1.1% margin over previous best method is achieved on DISFA, and can learn unbiased feature representation.