摘要
针对传统人脸表情识别算法存在的特征提取能力差、识别率低和误分类率较高等问题,提出一种基于生成对抗网络(GAN)改进的人脸表情识别算法.利用生成对抗网络的博弈思想,分别设计特征提取器、特征合成器和判别器,通过判别器与特征提取器之间的对抗训练,不断增强特征提取器提取特征的能力和分类器对人脸表情识别的准确率,并将其应用在工作人员工作状态智能监测中,根据表情识别结果判断工作状态,从而合理分配实验室资源,提高实验室资源利用率.改进算法在CK+数据集上多次实验的结果表明:该算法有较高的鲁棒性,能有效提高人脸表情识别率.
In view of the problems of traditional facial expression recognition algorithms, such as poor feature extraction ability, low recognition rate and high misclassification rate, we proposed an improved facial expression recognition algorithm based on generative adversarial network(GAN). Using the game theory of generating antagonism network, the feature extractor, feature combiner and discriminator were designed respectively. Through the adversarial training between the discriminator and feature extractor, the feature extraction ability of the feature extractor and the accuracy of the classifier for facial expression recognition were continuously enhanced, and it was applied to the intelligent monitoring of working state of staff. The working state was judged according to the results of expression recognition, so as to reasonably allocate the laboratory resources and improve the utilization rate of laboratory resources. The several experimental results of the improved algorithm on the CK+ dataset show that the proposed algorithm has higher robustness and can effectively improve the rate of facial expression recognition.
作者
李婷婷
胡玉龙
魏枫林
LI Tingting;HU Yulong;WEI Fenglin(Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,Jilin University,Changchun 130012,China;College of Computer Science and Technology,Jilin University,Changchun 130012,China;College of Software,Jilin University,Changchun 130012,China)
出处
《吉林大学学报（理学版）》
CAS
北大核心
2020年第3期605-610,共6页
Journal of Jilin University:Science Edition
基金
吉林省科技发展计划重点研发项目(批准号:20180201045GX).
关键词
表情识别
对抗生成网络
卷积神经网络
深度学习
工作状态智能监测
expression recognition
generative adversarial network(GAN)
convolutional neural network
deep learning
intelligent monitoring of working state
