基于性别约束的多分支网络人脸表情识别  

Multi-Branch Network Facial Expression Recognition Based on Gender Constraint

作　　者：曾曦 辛月兰 谢琪琦 ZENG Xi;XIN Yuelan;XIE Qiqi(College of Physics and Electronic Information Engineering,Qinghai Normal University,Xining 810000,China)

机构地区：[1]青海师范大学物理与电子信息工程学院,西宁810000

出　　处：《计算机工程与应用》2023年第9期245-254,共10页Computer Engineering and Applications

基　　金：国家自然科学基金(61662062);青海省自然科学基金(2022-ZJ-929)。

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%。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%.

关 键 词：人脸表情识别 性别约束 多分支网络 注意力机制 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]