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Micro-expression recognition with small sample size by transferring long-term convolutional neural network
Wang, Su-Jing1,8; Li, Bing-Jun2; Liu, Yong-Jin2; Yan, Wen-Jing3; Ou, Xinyu4; Huang, Xiaohua5; Xu, Feng6; Fu, Xiaolan7,8
Corresponding AuthorLiu, Yong-Jin(liuyongjin@tsinghua.edu.cn)
AbstractMicro-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.
KeywordMicro-expression Deep learning Transferring learning Convolutional neural network
2018-10-27
Language英语
DOI10.1016/j.neucom.2018.05.107
Source PublicationNEUROCOMPUTING
ISSN0925-2312
Volume312Pages:251-262
Indexed BySCI
Funding ProjectNational 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]
PublisherELSEVIER SCIENCE BV
WOS KeywordFACIAL EXPRESSIONS ; SCHIZOPHRENIA ; REMEDIATION ; DECEPTION ; LIES
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000438668100022
Citation statistics
Cited Times:16[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/27762
Collection中国科学院行为科学重点实验室
Corresponding AuthorLiu, Yong-Jin
Affiliation1.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
First Author AffilicationKey Laboratory of Behavioral Science, CAS
Recommended Citation
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.
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