MSSTNet: A Multi-Scale Spatio-Temporal CNN-Transformer Network for Dynamic Facial Expression Recognition

• URL: http://arxiv.org/abs/2404.08433v1
• Date: Fri, 12 Apr 2024 12:30:48 GMT
• Title: MSSTNet: A Multi-Scale Spatio-Temporal CNN-Transformer Network for Dynamic Facial Expression Recognition
• Authors: Linhuang Wang, Xin Kang, Fei Ding, Satoshi Nakagawa, Fuji Ren,
• Abstract summary: We propose a Multi-Scale-temporal CNN-Transformer network (MSSTNet) Our approach takes spatial different scales extracted by CNN and feeds them into a Multi-scale Embedding Layer (MELayer) The MELayer extracts multi-scale spatial information and encodes these features before sending them into a Transformer (T-Former)
• Score: 4.512502015606517
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unlike typical video action recognition, Dynamic Facial Expression Recognition (DFER) does not involve distinct moving targets but relies on localized changes in facial muscles. Addressing this distinctive attribute, we propose a Multi-Scale Spatio-temporal CNN-Transformer network (MSSTNet). Our approach takes spatial features of different scales extracted by CNN and feeds them into a Multi-scale Embedding Layer (MELayer). The MELayer extracts multi-scale spatial information and encodes these features before sending them into a Temporal Transformer (T-Former). The T-Former simultaneously extracts temporal information while continually integrating multi-scale spatial information. This process culminates in the generation of multi-scale spatio-temporal features that are utilized for the final classification. Our method achieves state-of-the-art results on two in-the-wild datasets. Furthermore, a series of ablation experiments and visualizations provide further validation of our approach's proficiency in leveraging spatio-temporal information within DFER.

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