基于性别约束的多分支网络人脸表情识别

doi:10.3778/j.issn.1002-8331.2202-0120

摘要/Abstract

摘要： 针对不同性别下人脸表情类内变化大、类间差异小的问题，提出一种基于性别约束的多分支网络人脸表情识别方法。通过聚类算法K-means与卷积神经网络相结合的方法，得到性别约束下人脸表情类间关系。根据类间关系，构建主干网络和具有通道注意力机制的分支网络，进一步区分强相似的类间关系和突出不同性别人脸表情的类内变化。最后在CK+、FER2013和RAF-DB数据集上进行实验并分析。实验表明，提出的网络结构在CK+、FER2013和RAF-DB数据集上的平均识别率均优于其他先进方法，分别达到了97.60%、73.58%和87.98%。

关键词: 人脸表情识别, 性别约束, 多分支网络, 注意力机制

Abstract: Aiming at the large intra-class variation and small inter-class differences of facial expressions under different genders, the thesis proposes a multi-branch network facial expression recognition based on gender constraints. Firstly, through the method of clustering algorithm K-means and convolutional neural network, the relationship between facial expression classes under gender constraints is obtained. Then, according to the obtained inter-class relationships, a backbone network and a branch network with a channel attention mechanism are constructed to further distinguish between strongly similar inter-class relationships and highlight intra-class changes in facial expressions of different genders. Finally, experiments and analysis are performed on CK+, FER2013 and RAF-DB datasets. Experiments show that the average recognition rate of the proposed network structure on the CK+, FER2013 and RAF-DB datasets is superior to other advanced methods, reaching 97.60%, 73.58% and 87.98%.

Key words: facial expression recognition, gender constraint, multi-branch network, attention mechanism

曾曦, 辛月兰, 谢琪琦. 基于性别约束的多分支网络人脸表情识别[J]. 计算机工程与应用, 2023, 59(9): 245-254.

ZENG Xi, XIN Yuelan, XIE Qiqi. Multi-Branch Network Facial Expression Recognition Based on Gender Constraint[J]. Computer Engineering and Applications, 2023, 59(9): 245-254.

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