摘要
为了提高表情识别技术的检测精度,文中提出了一种采用随机权重平均SWA优化级联网络的人脸表情识别方法。与单个卷积网络相比,多网络级联能得到更好的检测精度。相对于传统的SGD训练方法,SWA训练方法能增强级联网络中子网络的泛化能力,进一步提高模型的整体性能。通过在Fer2013数据集上测试实验发现,基于SWA方法训练采用加权求和法方式级联的网络模型识别准确率达到74.478%,相对于传统SGD方法训练的单网络模型提高了1.4%以上。另外,与其他典型方法相比,所提改进模型的识别准确率更高。
In order to improve the detection accuracy of expression recognition technology,a face expression recognition method based on cascade network optimized by SWA is proposed.Compared with a single convolutional network,multi-network cascading reachs higher detection accuracy.With respect to the traditional training method SGD,SWA training method enhances the generalization ability of the sub-network in the cascade network,which further improves the overall performance of the model.By testing on the Fer2013 dataset,the experimental results shows that the detection accuracy of the network cascaded by the way of weighted summation based on SWA training method reachs 74.478%,which is 1.4%higher than the single network model trained by the traditional SGD method.In addition,the improved model proposed in the present study reachs a higher recognition accuracy than other typical methods.
作者
张翔
史志才
陈良
ZHANG Xiang;SHI Zhicai;CHEN Liang(School of Electronic and Electrical Engineering,Shanghai University of Engineering Science,Shanghai 201620,China)
出处
《电子科技》
2020年第9期16-20,共5页
Electronic Science and Technology
基金
国家自然科学基金(61802252)。
关键词
表情识别
卷积神经网络
随机权重平均
随机梯度下降法
Fer2013数据集
网络级联
expression recognition
convolutional neural network
stochastic weight averaging
stochastic gradient descent
Fer2013 dataset
network cascade
