Research Article
Facial Expression Recognition Based on Deep Spatio-Temporal Attention Network
@INPROCEEDINGS{10.1007/978-3-031-24386-8_28, author={Shuqin Li and Xiangwei Zheng and Xia Zhang and Xuanchi Chen and Wei Li}, title={Facial Expression Recognition Based on Deep Spatio-Temporal Attention Network}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 18th EAI International Conference, CollaborateCom 2022, Hangzhou, China, October 15-16, 2022, Proceedings, Part II}, proceedings_a={COLLABORATECOM PART 2}, year={2023}, month={1}, keywords={Facial expression recognition Spatio-temporal features Deep attention network}, doi={10.1007/978-3-031-24386-8_28} }- Shuqin Li
Xiangwei Zheng
Xia Zhang
Xuanchi Chen
Wei Li
Year: 2023
Facial Expression Recognition Based on Deep Spatio-Temporal Attention Network
COLLABORATECOM PART 2
Springer
DOI: 10.1007/978-3-031-24386-8_28
Abstract
Facial expression recognition is extremely critical in the process of human-computer interaction. Existing facial expression recognition tends to focus on a single feature of the face and does not take full advantage of the integrated spatio-temporal features of facial expression images. Therefore, this paper proposes a facial expression recognition based on a deep spatio-temporal attention network (STANER) to capture the spatio-temporal features of facial expressions when they change subtly. A facial expression recognition with an attention module based on spatial global features (SGAER) is created firstly, where the addition of the attention module is able to quantify the importance of each part of the expression feature map and thus extract the spatial global appearance features at the time of subtle expression changes from a single frame expression image. Then, facial expression recognition with C-LSTM based on temporal local features (TLER) is built to process image sequences of facial regions linked to expression creation and extract dynamic local temporal information about expressions. Experiments are carried out on CK+ and Oulu-CASIA datasets. The results showed that STANER can achieve better performance with the accuracy rates of 98.23(\%)and 89.52(\%)on the two mainstream datasets, respectively.
