Affiliations: [a] School of Data Science and Information Engineering and Guizhou Key Laboratory of Pattern Recognition and Intelligent System, Guizhou Minzu University, Guiyang, China
| [b] School of Mathematics and Statistics, Qiannan Normal University for Nationalities and Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province, Duyun, China
| [c] Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan, China
| [d] Guizhou Aerospace Tianma Electromechanical Technology Co, LTD Zunyi, China
Correspondence:
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Corresponding author. Lin Wang, School of Data Science and Information Engineering and Guizhou Key Laboratory of Pattern Recognition and Intelligent System, Guizhou Minzu University, Guiyang, China. E-mail: wanglin@gzmu.edu.cn.
Note: [1] These authors contributed equally to this work and should be considered co-first authors.
Abstract: Although facial expression recognition (FER) has a wide range of applications, it may be difficult to achieve under local occlusion conditions which may result in the loss of valuable expression features. This issue has motivated the present study, as a part of which an effective multi-feature cross-attention network (MFCA-Net) is proposed. The MFCA-Net consists of a two-branch network comprising a multi-feature convolution module and a local cross-attention module. Thus, it enables decomposition of facial features into multiple sub-features by the multi-feature convolution module to reduce the impact of local occlusion on facial expression feature extraction. In the next step, the local cross-attention module distinguishes between occluded and unoccluded sub-features and focuses on the latter to facilitate FER. When the MFCA-Net performance is evaluated by applying it to three public large-scale datasets (RAF-DB, FERPlus, and AffectNet), the experimental results confirm its good robustness. Further validation is performed on a real FER dataset with local occlusion of the face.
Keywords: Facial expression recognition, deep convolution, multi-feature convolution module, local cross-attention module
