Cross-Dataset Facial Expression Recognition based on Arousal-Valence Emotion Model and Transfer Learning Method

Traditional facial expression recognition methods assume that facial expression in the training and testing sets are collected under the same condition such that they are independent and identically distributed. However, the assumption is not satisfied in many real applications. This problem is referred to as cross-dataset facial expression recognition. On the other hand, the traditional facial expression recognition methods are based on basic emotion theory proposed by Ekman. Unfortunately, the theory is limited to express diverse and subtle emotion. To solve the problem of the cross-dataset facial expression recognition and enrich the emotion expression, a transfer learning algorithm TPCA and arousal-valence emotional model are adopted in this paper. A new facial emotion recognition method based on TPCA and two-level fusion is proposed, which combine weight fusion and correlation fusion between arousal and valence to improve the recognition performance under cross-dataset scenarios. The contrast experimental results show that the proposed method can get better recognition result than the traditional methods.