Transferring Dual Stochastic Graph Convolutional Network for Facial Micro-expression Recognition

TL;DR

This paper introduces a novel transferring dual stochastic graph convolutional network that leverages transfer learning, stochastic graph construction, and fusion of spatial-temporal features to enhance micro-expression recognition accuracy.

Contribution

It is the first to combine transfer learning with graph convolutional networks for micro-expression recognition, addressing class imbalance and improving performance.

Findings

Achieved state-of-the-art results on SAMM and MMEW benchmarks.

Effectively handled class imbalance with focal loss.

Demonstrated the benefit of integrating spatial and temporal features.

Abstract

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.