An improved micro-expression recognition algorithm of 3D convolutional neural network

作     者：WU Jin SHI Qianwen XI Meng WANG Lei ZENG Huadie 吴进;SHI Qianwen;XI Meng;WANG Lei;ZENG Huadie

作者机构：School of Electronic and EngineeringXi’an University of Posts and TelecommunicationsXi’an 710121P.R.China 

出 版 物：《High Technology Letters》 (高技术通讯（英文版）)

年 卷 期：2022年第28卷第1期

页      面：63-71页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 080203[工学-机械设计及理论] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Supported by the Shaanxi Province Key Research and Development Project(No.2021GY-280) Shaanxi Province Natural Science Basic Re-search Program Project(No.2021JM-459) the National Natural Science Foundation of China(No.61834005,61772417,61802304,61602377,61634004) the Shaanxi Province International Science and Technology Cooperation Project(No.2018KW-006) 

主　　题：micro-expression recognition deep learning three-dimensional convolutional neural network(3D-CNN) batch normalization(BN)algorithm dropout 

摘      要：The micro-expression lasts for a very short time and the intensity is very *** at the problem of its low recognition rate,this paper proposes a new micro-expression recognition algorithm based on a three-dimensional convolutional neural network(3D-CNN),which can extract two-di-mensional features in spatial domain and one-dimensional features in time domain,*** network structure design is based on the deep learning framework Keras,and the discarding method and batch normalization(BN)algorithm are effectively combined with three-dimensional vis-ual geometry group block(3D-VGG-Block)to reduce the risk of overfitting while improving training *** at the problem of the lack of samples in the data set,two methods of image flipping and small amplitude flipping are used for data ***,the recognition rate on the data set is as high as 69.11%.Compared with the current international average micro-expression recog-nition rate of about 67%,the proposed algorithm has obvious advantages in recognition rate.