基于集成卷积神经网络的面部表情分类  

Facial Expression Classification Based on Ensemble Convolutional Neural Network

作　　者：周涛 吕晓琪 任国印[1] 谷宇 张明 李菁[1] Zhou Tao;Lü Xiaoqi;Ren Guoyin;Gu Yu;Zhang Ming;Li Jing(Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Progressing,School of Information Engineering,Inner Mongolia University of Science and Technology,Baotou,Inner Mongolia 014010,China;School of Information Engineering,Inner Mongolia University of Technology,Hohhot,Inner Mongolia 010051,China;School of Computer Engineering and Science,Shanghai University,Shanghai 200444,China;Information Science and Technology College,Dalian Maritime University,Dalian,Liaoning 116026,China)

机构地区：[1]内蒙古科技大学信息工程学院模式识别与智能图像处理重点实验室,内蒙古包头014010 [2]内蒙古工业大学信息工程学院,内蒙古呼和浩特010051 [3]上海大学计算机工程与科学学院,上海200444 [4]大连海事大学信息科学技术学院,辽宁大连116026

出　　处：《激光与光电子学进展》2020年第14期316-327,共12页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(61771266,61841204,61179019,61962046,61663036);内蒙古自治区自然科学基金(2019MS06003,2015MS0604);内蒙古自治区自然科学基金杰出青年培育基金项目(2018JQ02);内蒙古自治区高等学校科学技术研究项目(NJZY145,NJZY18150);包头市科技计划项目(2017C1002,2015C2006-14);包头市青年人才创新基金项目(0701011904);内蒙古自治研究生科研创新项目(S2018112028Z);教育部“春晖计划”合作科研项目(教外司留[2019]1383号)。

摘　　要：针对传统机器学习中人工提取特征复杂度高,以及单卷积网络提取特征不充分导致识别率不高的问题,提出了一种基于集成卷积神经网络的面部表情识别新方法。该方法是将VGGNet-19改进后的VGGNet-19GP模型和ResNet-18模型进行集成,构建了集成网络(EnsembleNet)模型。该模型首先在训练集上对单模型进行训练,使单模型达到实验最优,然后在测试集上进行集成测试。在FER2013和CK+数据集上分别获得了73.854%和97.611%的平均准确率。与VGGNet-19GP和ResNet-18模型以及现有方法进行对比,结果表明,基于集成的面部表情分类方法具有分类更加准确和泛化能力更强的优点。In view of the high complexity of artificial feature extraction in traditional machine learning and the low recognition rate caused by inadequate feature extraction in single convolutional network,a new facial expression recognition method based on ensemble convolutional neural network is proposed.The method is to construct an ensemble network(EnsembleNet)model based on integrating an improved VGGNet-19GP model after VGGNet-19 with a ResNet-18 model.The model first trains a single model on the training set to make the single model reach the optimal experiment.Then the ensemble test is performed on the testing set.The average accuracy of 73.854%and 97.611%are obtained on FER2013 and CK+datasets,respectively.By comparison with the VGGNet-19GP and ResNet-18 models and other existing methods,it is shown that the ensemble-based facial expression classification method has the advantages of more accurate classification and stronger generalization ability.

关 键 词：机器视觉 人脸表情识别 卷积神经网络 集成学习 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]