基于改进的深度残差网络的表情识别研究  

Research on expression recognition based on improved deep residual network

作　　者：何俊[1] 刘跃 李倡洪[1] 沈津铭 李帅 王京威 He Jun;Liu Yue;Li Changhong;Shen Jinming;Li Shuai;Wang Jingwei(School of Information Engineering,Nanchang University,Nanchang 330031,China)

机构地区：[1]南昌大学信息工程学院,南昌330031

出　　处：《计算机应用研究》2020年第5期1578-1581,共4页Application Research of Computers

基　　金：国家自然科学基金资助项目(61463034)。

摘　　要：提出了一种基于改进的深度残差网络(residual network,ResNet)的表情识别算法。采用小卷积核和深网络结构,利用残差模块学习残差映射解决了随着网络深度增加网络精度下降的问题,通过迁移学习方法克服了因数据量不足导致训练不充分的缺点;网络架构使用了线性支持向量机(SVM)进行分类。实验中首先利用ImageNet数据库进行网络参数预训练,使网络具有良好的提取特征能力,根据迁移学习方法,利用FER-2013数据库以及扩充后的CK+数据库进行参数微调和训练。该算法克服了浅层网络需要依靠手工特征,深层网络难以训练等问题,在CK+数据库以及GENKI-4K数据库上分别取得了91.333%和95.775%识别率。SVM在CK+数据库的分类效果较softmax提高了1%左右。This paper proposed an improved residual network(ResNet)expression recognition algorithm.The algorithm used small convolution kernels and a deep network structure to solve the problem of accuracy reduction with the increase of depth by the residual module.The experiment overcame the shortcoming of insufficient data through transfer learning,which could effectively prevent overfitting.The network architecture used a linear SVM for classification.The experiment used the ImageNet database to pre-train network parameters to have an excellent ability to extract feature.According to transfer learning,the algorithm used the FER-2013 database and the expanded CK+database to fine-tune and train network parameters,and overcame the problem that shallow networks rely on manual features and deep networks were difficult to train.The results show the recognition rates is 91.333%and 95.775%on the CK+database and the GENKI-4 K database,respectively.The classification accuracy of SVM in CK+database is about 1%higher than that of softmax.

关 键 词：深度学习 残差网络 表情识别 迁移学习 支持向量机 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]