Micro-expression recognition with small sample size by transferring long-term convolutional neural network

doi:10.1016/j.neucom.2018.05.107

	Micro-expression recognition with small sample size by transferring long-term convolutional neural network
	Wang,Su-Jing 1,8; Li,Bing-Jun 2; Liu,Yong-Jin 2; Yan,Wen-Jing 3; Ou,Xinyu 4; Huang,Xiaohua 5; Xu,Feng 6; Fu,Xiaolan7,8
第一作者	Wang, Su-Jing
通讯作者	Liu, Yong-Jin(liuyongjin@tsinghua.edu.cn)
通讯作者邮箱	liuyongjin@tsinghua.edu.cn
心理所单位排序	7
摘要	Micro-expression is one of important clues for detecting lies. Its most outstanding characteristics include short duration and low intensity of movement. Therefore, video clips of high spatial-temporal resolution are much more desired than still images to provide sufficient details. On the other hand, owing to the difficulties to collect and encode micro-expression data, it is small sample size. In this paper, we use only 560 micro-expression video clips to evaluate the proposed network model: Transferring Long-term Convolutional Neural Network (TLCNN). TLCNN uses Deep CNN to extract features from each frame of micro-expression video clips, then feeds them to Long Short Term Memory (LSTM) which learn the temporal sequence information of micro-expression. Due to the small sample size of micro-expression data, TLCNN uses two steps of transfer learning: (1) transferring from expression data and (2) transferring from single frame of micro-expression video clips, which can be regarded as "big data". Evaluation on 560 micro-expression video clips collected from three spontaneous databases is performed. The results show that the proposed TLCNN is better than some state-of-the-art algorithms. (C) 2018 Elsevier B.V. All rights reserved.
关键词	Micro-expression Deep learning Transferring learning Convolutional neural network
	2018-10-27
语种	英语
DOI	10.1016/j.neucom.2018.05.107
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
卷号	312 页码:251-262
URL	查看原文
收录类别	SCI
资助项目	National Natural Science Foundation of China[61772511] ; National Natural Science Foundation of China[61379095] ; National Natural Science Foundation of China[U1736220] ; National Natural Science Foundation of China[61725204]
出版者	ELSEVIER SCIENCE BV
WOS关键词	FACIAL EXPRESSIONS ; SCHIZOPHRENIA ; REMEDIATION ; DECEPTION ; LIES
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000438668100022
WOS分区	Q1
测试或任务	micro-expression recognition; Transferring Long-term Convolutional Neural Network (TLCNN)
因变量指标	recognition accuracy
资助机构	National Natural Science Foundation of China
引用统计	被引频次：75[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.psych.ac.cn/handle/311026/27762
专题	中国科学院行为科学重点实验室
作者单位	1.Inst Psychol, CAS Key Lab Behav Sci, Beijing 100101, Peoples R China; 2.Tsinghua Univ, Dept Comp Sci & Technol, Beijing Natl Res Ctr Informat Sci & Technol, Beijing, Peoples R China; 3.Northeastern Univ, Coll Informat Sci & Engn, Shenyang, Liaoning, Peoples R China; 4.Yunnan Open Univ, Cadres Online Learning Inst Yunnan Prov, Kunming 650223, Yunnan, Peoples R China; 5.Univ Oulu, Faulty Informat Technol & Elect Engn, Ctr Machine Vis & Signal Anal, POB 4500, FI-90014 Oulu, Finland; 6.Fudan Univ, Sch Comp Sci, Key Lab Informat Sci Electromagnet Waves MoE, Shanghai Key Lab Intelligent Informat Proc, Shanghai 200433, Peoples R China; 7.Chinese Acad Sci, Inst Psychol, State Key Lab Brain & Cognit Sci, Beijing 100101, Peoples R China; 8.Univ Chinese Acad Sci, Dept Psychol, Beijing 100049, Peoples R China
第一作者单位	中国科学院行为科学重点实验室
推荐引用方式 GB/T 7714	Wang,Su-Jing,Li,Bing-Jun,Liu,Yong-Jin,et al. Micro-expression recognition with small sample size by transferring long-term convolutional neural network[J]. NEUROCOMPUTING,2018,312:251-262.
APA	Wang,Su-Jing.,Li,Bing-Jun.,Liu,Yong-Jin.,Yan,Wen-Jing.,Ou,Xinyu.,...&Fu,Xiaolan.(2018).Micro-expression recognition with small sample size by transferring long-term convolutional neural network.NEUROCOMPUTING,312,251-262.
MLA	Wang,Su-Jing,et al."Micro-expression recognition with small sample size by transferring long-term convolutional neural network".NEUROCOMPUTING 312(2018):251-262.

条目包含的文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
Micro-expression rec（2521KB）	期刊论文	出版稿	限制开放	CC BY-NC-SA	请求全文