Motion Attention Deep Transfer Network for Cross-database Micro-expression Recognition

Wanchuang Xia, Wenming Zheng, Yuan Zong, Xingxun Jiang

Abstract: Cross-database micro-expression recognition is a great challenging problem due to the short duration and low intensity of micro-expressions from different collection conditions. In this paper, we present a Motion Attention Deep Transfer Network (MADTN) that can focus on the most discriminative movement regions of the face and reduce the database bias. Specifically, we firstly combine the motion information and facial appearance information to obtain the discriminative representation by merging the optical flow fields between three key-frames (the onset frame, the middle frame, the offset frame) and the facial appearance of the middle frame. Then, the deep network architecture extracts cross-domain feature with the superiority of the maximum mean discrepancy(MMD) loss so that the source and target domains have a similar distribution. Results on benchmark cross-database micro-expression experiments demonstrate that the MADTN achieves remarkable performance in many micro-expression transfer tasks and exceed the state-of-the-art results, which show the robustness and superiority of our approach.