摘要
针对目前人脸表情识别的准确率偏低、训练速度较慢、泛化能力弱等问题,提出了改进的VGGNet,添加BN算法和PReLU激活函数,在图像预处理时加入高斯滤波和直方图均衡化,并且使用FER2013、AffectNet、JAFFE、CK+四种数据集进行比较分析。最终的实验结果表明,该模型在四种数据集上的识别准确率都有所提高,在四种数据集上的准确率达到73.52%、84.66%、94.28%、95.26%。在测试集上的泛化能力较强,训练速度也变快。
In view of the low accuracy,slow training speed,and weak generalization ability of facial expression recognition at present.An improved VGGNet is proposed,adds Batch Normalization(BN)algorithm and PReLU activation function.Gaussian filtering and histogram equalization are used in image preprocessing,and four data sets of FER2013,AffectNet,JAFFE,CK+are used for comparative analysis.The final experimental results show that the recognition accuracy of the model has been improved on the four data sets,and the accuracy of the mode on the four data set has reached 73.52%,84.66%,94.28%and 95.26%.The generalization ability of the model on the test sets is strong,and the training speed is also faster.
作者
张士豹
王文韬
ZHANG Shibao;WANG Wentao(Nanjing University of Information Science&Technology,Nanjing 210044,China)
出处
《现代信息科技》
2021年第23期100-103,共4页
Modern Information Technology
关键词
卷积神经网络
激活函数
BN算法
表情识别
convolutional neural network
activation function
BN algorithm
facial expression recognition
