基于改进MobileNetV2的人脸表情识别  

Facial expression recognition based on improved MobileNetV2

作　　者：严春满[1] 张翔 王青朋 YAN Chun-man;ZHANG Xiang;WANG Qing-peng(College of Physics and Electronic Engineering,Northwest Normal University,Lanzhou 730070,China)

机构地区：[1]西北师范大学物理与电子工程学院,甘肃兰州730070

出　　处：《计算机工程与科学》2023年第6期1071-1078,共8页Computer Engineering & Science

基　　金：国家自然科学基金(61961037);甘肃省教育厅2021年度产业支撑计划(2021CYZC-30)。

摘　　要：针对现有深度卷积神经网络参数量庞大,导致人脸表情识别场景受限的问题,提出一种基于改进轻量级卷积神经网络的人脸表情识别模型。该模型以MobileNetV2轻量级特征提取网络为主要框架,通过压缩网络宽度因子与整体维度,减少网络参数量与计算量;引入SandGlass模块对网络倒残差模块进行改进,减少特征信息在网络传输中的丢失;同时嵌入高效通道注意力机制,提高网络对于特征信息的提取能力。在人脸表情数据集FER2013和CK+上进行实验,所提网络模型的人脸表情识别准确率达到了68.96%与95.96%,分别高于MobileNetV21.06%与6.14%,且参数量下降82.28%,实验结果验证了网络模型改进措施的有效性。Aiming at the problem that the existing deep convolutional neural network has a large amount of parameters,which leads to the limitation of facial expression recognition scenes,this paper proposes a facial expression recognition model based on improved lightweight convolutional neural network.The model takes MobileNetV2 lightweight feature extraction network as the main framework,by compressing the network width factor and the global dimension,the number of network parameters and the amount of computation are reduced.SandGlass block is introduced to improve the reverse residual module in this network,and reduce the loss of feature information during network transmission.At the same time,the efficient channel attention mechanism is embedded to improve the network's ability to extract feature information.Experiments were carried out on the facial expression data sets FER2013 and CK+.The facial expression accuracy rate of the proposed network reaches 68.96%and 95.96%,which are 1.06%and 6.14%higher than that of MobileNetV2 respectively,and the number of parameters are decreased by 82.28%.Experimental results verify the effectiveness of the improved network model.

关 键 词：人脸表情识别 轻量级网络 MobileNetV2 倒残差模块 通道注意力 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]