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

IF 17.3 3区计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING Computational Visual Media Pub Date : 2024-02-08 DOI:10.1007/s41095-023-0369-x

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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. Abstract Image

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CF-DAN：基于交叉融合双注意网络的面部表情识别

摘要最近，面部表情识别（FER）主要侧重于野外图像，包括人脸遮挡和图像模糊等因素，而不是实验室图像。复杂的野外环境给 FER 带来了新的挑战。为了应对这些挑战，本研究提出了一种交叉融合双注意网络。该网络由三部分组成：(1) 交叉融合分组双注意机制，用于提炼局部特征并获取全局信息；(2) 提出的 C2 激活函数构造方法，即具有三个自由度的片断三次多项式，需要的计算量更少，灵活性和识别能力更强，能较好地解决运行速度慢和神经元失活的问题；(3) 自注意提炼过程与残余连接之间的闭环操作，用于抑制冗余信息，提高模型的泛化能力。在 RAF-DB、FERPlus 和 AffectNet 数据集上的识别准确率分别为 92.78%、92.02% 和 63.58%。实验表明，该模型能为 FER 任务提供更有效的解决方案。

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来源期刊

Computational Visual Media Computer Science-Computer Graphics and Computer-Aided Design

CiteScore

16.90

自引率

5.80%

发文量

243

审稿时长

6 weeks

期刊介绍： Computational Visual Media is a peer-reviewed open access journal. It publishes original high-quality research papers and significant review articles on novel ideas, methods, and systems relevant to visual media. Computational Visual Media publishes articles that focus on, but are not limited to, the following areas: • Editing and composition of visual media • Geometric computing for images and video • Geometry modeling and processing • Machine learning for visual media • Physically based animation • Realistic rendering • Recognition and understanding of visual media • Visual computing for robotics • Visualization and visual analytics Other interdisciplinary research into visual media that combines aspects of computer graphics, computer vision, image and video processing, geometric computing, and machine learning is also within the journal''s scope. This is an open access journal, published quarterly by Tsinghua University Press and Springer. The open access fees (article-processing charges) are fully sponsored by Tsinghua University, China. Authors can publish in the journal without any additional charges.

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