  Micro-expression recognition has drawn increasing attention due to its wide
application in lie detection, criminal detection and psychological
consultation. To improve the recognition performance of the small
micro-expression data, this paper presents a transferring dual stochastic Graph
Convolutional Network (TDSGCN) model. We propose a stochastic graph
construction method and dual graph convolutional network to extract more
discriminative features from the micro-expression images. We use transfer
learning to pre-train SGCNs from macro expression data. Optical flow algorithm
is also integrated to extract their temporal features. We fuse both spatial and
temporal features to improve the recognition performance. To the best of our
knowledge, this is the first attempt to utilize the transferring learning and
graph convolutional network in micro-expression recognition task. In addition,
to handle the class imbalance problem of dataset, we focus on the design of
focal loss function. Through extensive evaluation, our proposed method achieves
state-of-the-art performance on SAMM and recently released MMEW benchmarks. Our
code will be publicly available accompanying this paper.
