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

• URL: http://arxiv.org/abs/2203.05208v1
• Date: Thu, 10 Mar 2022 07:41:18 GMT
• Title: Transferring Dual Stochastic Graph Convolutional Network for Facial Micro-expression Recognition
• Authors: Hui Tang, Li Chai, Wanli Lu
• Abstract summary: This paper presents a transferring dual Graph Convolutional Network (GCN) model. We propose a graph construction method and dual graph convolutional network to extract more discriminative features from the micro-expression images. Our proposed method achieves state-of-the-art performance on recently released MMEW benchmarks.
• Score: 7.62031665958404
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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