自动化学报2018，Vol.44Issue(1)：176-182,7.DOI:10.16383/j.aas.2018.c160835
基于跨连接LeNet-5网络的面部表情识别
Facial Expression Recognition with Cross-connect LeNet-5 Network
摘要
Abstract
In order to avoid the influence of human factors on facial expression feature extraction, convolution neural network is adopted for facial expression recognition in this paper. Compared with the traditional method of facial expression recognition which requires complicated manual feature extraction, convolutional neural network can omit the process of feature extraction. The classical LeNet-5 convolutional neural network has a good recognition rate in handwritten digital dataset,but a low recognition rate in facial expression recognition. An improved LeNet-5 convolution neural network is proposed for facial expression recognition, which combines low-level features with high-level features extracted from the network structure to construct the classifier. The method achieves good results in JAFFE expression dataset and the CK+dataset.关键词
卷积神经网络/面部表情识别/特征提取/跨连接Key words
Convolutional neural network/facial expression recognition/feature extraction/cross-connect引用本文复制引用
李勇,林小竹,蒋梦莹..基于跨连接LeNet-5网络的面部表情识别[J].自动化学报,2018,44(1):176-182,7.基金项目
国家自然科学基金(60772168)资助Supported by National Natural Science Foundation of China(60772168) （60772168）
