A Multi-task Mean Teacher for Semi-supervised Facial Affective Behavior Analysis

Affective Behavior Analysis is an important part in human-computer interaction. Existing successful affective behavior analysis method such as TSAV[9] suffer from challenge of incomplete labeled datasets. To boost its performance, this paper presents a multi-task mean teacher model for semi-supervised Affective Behavior Analysis to learn from missing labels and exploring the learning of multiple correlated task simultaneously. To be specific, we first utilize TSAV as baseline model to simultaneously recognize the three tasks. We have modified the preprocessing method of rendering mask to provide better semantics information. After that, we extended TSAV model to semi-supervised model using mean teacher, which allow it to be benefited from unlabeled data. Experimental results on validation datasets show that our method achieves better performance than TSAV model, which verifies that the proposed network can effectively learn additional unlabeled data to boost the affective behavior analysis performance.