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Research Article | Open Access

CF-DAN: Facial-expression recognition based on cross-fusion dual-attention network

Shandong Technology and Business University, Shandong 264005, China
School of Information and Electrical Engineering, Ludong University, Yantai 264025, China
Shangdong University, Shandong 250100, China
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Graphical Abstract

Abstract

Recently, facial-expression recognition (FER) has primarily focused on images in the wild, including factors such as face occlusion and image blurring, rather than laboratory images. Complex field environments have introduced new challenges to FER. To address these challenges, this study proposes a cross-fusion dual-attention network. The network comprises three parts: (1) a cross-fusion grouped dual-attention mechanism to refine local features and obtain global information; (2) a proposed C2 activation function construction method, which is a piecewise cubic polynomial with three degrees of freedom, requiring less computation with improved flexibility and recognition abilities, which can better address slow running speeds and neuron inactivation problems; and (3) a closed-loop operation between the self-attention distillation process and residual connections to suppress redundant information and improve the generalization ability of the model. The recognition accuracies on the RAF-DB, FERPlus, and AffectNet datasets were 92.78%, 92.02%, and 63.58%, respectively. Experiments show that this model can provide more effective solutions for FER tasks.

Keywords

facial-expression recognition (FER)cubic polynomial activation functiondual-attention mechanisminteractive learningself-attention distillation

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Computational Visual Media
Volume 10 Issue 3,
June 2024
Pages 593-608
DOI: 10.1007/s41095-023-0369-x
Cite this article:
Zhang F, Chen G, Wang H, et al. CF-DAN: Facial-expression recognition based on cross-fusion dual-attention network. Computational Visual Media, 2024, 10(3): 593-608. https://doi.org/10.1007/s41095-023-0369-x

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Received: 21 February 2023
Accepted: 19 July 2023
Published: 08 February 2024
© The Author(s) 2024.

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