Micro-Expression Recognition Based on Dual-Stream Networks Information Interaction

Scarcity of experimental databases for deep learning based micro-expression recognition leads to limited knowledge acquisition in learning process and increasing difficulty for improving accuracy and generalization capabilities. In view of the problem, a micro-expression algorithm based on dual-stream network information interaction is proposed. An improved deep mutual learning strategy is designed to guide the interactive training between different modalities of the image sequence to improve the recognition rate of the network, in which the main network is built based on the RGB image sequence, and the auxiliary network is built based on the optical flow. In the training phase, the training process is optimized via a mutual learning loss which includes the supervised learning loss and mimic loss, which ensures the prediction of each mode is consistent with the true identity of the training sample and the predictions of other modalities. In the testing phase, since the mutual learning mechanism enhances the discrimination ability of RGB branches, the optical flow branches can be tailored to improve the recognition speed while ensuring accuracy. The experimental results on the CASME, CASME Ⅱ and SMIC data sets show that the algorithm effectively improves the recognition accuracy and the overall performance is better than existing algorithms.