ATB-Net: attention-based network for facial expression recognition

Lanfang Dong; Yingchao Tang; Puzhao Hu; Meng Mao; Guoming Li; Linxiang Tan

doi:10.1117/12.3009441

11 October 2023 ATB-Net: attention-based network for facial expression recognition

Lanfang Dong, Yingchao Tang, Puzhao Hu, Meng Mao, Guoming Li, Linxiang Tan

Proceedings Volume 12918, Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023); 129181W (2023) https://doi.org/10.1117/12.3009441
Event: International Conference on Computer Science and Communication Technology (ICCSCT 2023), 2023, Wuhan, China

Abstract

Recent years, the related research on facial expression recognition is increasingly attracting people’s attention. As an important branch of artificial intelligence, facial expression recognition has a significant role in human-computer interaction and other related fields. However, due to the high similarity between different expressions and the small interclass differences, it is still a daunting task for researchers to obtain a robust and accurate expression recognition model. Considering the characteristics of human expressions, a new convolution mechanism is proposed, which we call the attention convolution module. We also apply the convolution module to the field of expression recognition and propose a new expression recognition network (ATB-NET). The experimental results show that our method can focus on relatively important regions in the face, besides, compared with previous work, it also has a significant improvement on multiple public data sets (such as FER2013, CK+).

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lanfang Dong, Yingchao Tang, Puzhao Hu, Meng Mao, Guoming Li, and Linxiang Tan "ATB-Net: attention-based network for facial expression recognition", Proc. SPIE 12918, Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023), 129181W (11 October 2023); https://doi.org/10.1117/12.3009441

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KEYWORDS

Facial recognition systems

Convolution

Machine learning

Data modeling

Matrices

Emotion

Education and training

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