Facial micro-expression recognition using stochastic graph convolutional network and dual transferred learning

Abstract

Micro-expression recognition (MER) has drawn increasing attention due to its wide application in lie detection, criminal detection and psychological consultation. However, the best recognition accuracy on recent public dataset is still low compared to the accuracy of macro-expression recognition. In this paper, we propose a novel graph convolution network (GCN) for MER achieving state-of-the-art accuracy. Different to existing GCN with fixed graph structure, we define a stochastic graph structure in which some neighbors are selected randomly. As shown by numerical examples, randomness enables better feature characterization while reducing computational complexity. The whole network consists of two branches, one is the spatial branch taking micro-expression images as input, the other is the temporal branch taking optical flow images as input. Because the micro-expression dataset does not have enough images for training the GCN, we employ the transfer learning mechanism. That is, different stochastic GCNs (SGCN) have been trained by the macro-expression dataset in the source network. Then the well-trained SGCNs are transferred to the target network. It is shown that our proposed method achieves the state-of-art performance on all four well-known datasets. This paper explores stochastic GCN and transfer learning with this random structure in the MER task, which is of great importance to improve the recognition performance.