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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
First AuthorWang, Su-Jing
2018-10-27
Source PublicationNeurocomputing
Correspondent Emailliuyongjin@tsinghua.edu.cn
ISSN09252312
Subtypearticle
Volume312Pages:251-262
Contribution Rank1
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. © 2018 Elsevier B.V.
KeywordMicro-expression  Deep learning  Transferring learning  Convolutional neural network
Subject AreaBig Data - Convolution - Deep Learning - Image Retrieval - Video Cameras
MOST Discipline CatalogueLong short-term memory
DOI10.1016/j.neucom.2018.05.107
URL查看原文
Indexed BySSCI ; EI
Language英语
Project Intro.This paper is supported in part by grants from the National Natural Science Foundation of China (61772511, 61379095, U1736220, 61725204).
PublisherElsevier B.V.
Citation statistics
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.CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing; 100101, China;
2.Beijing National Research Center for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing, China;
3.College of Information Science and Engineering, Northeastern University, Shenyang, China;
4.Cadres Online Learning Institute of Yunnan Province, Yunnan Open University, Kunming; 650223, China;
5.Center for Machine Vision and Signal Analysis, Faulty of Information Technology and Electrical Engineering, University of Oulu, P. O. Box 4500, FI-90014, Finland;
6.Shanghai Key Laboratory of Intelligent Information Processing, Key Laboratory for Information Science of Electromagnetic Waves (MoE), School of Computer Science, Fudan University, Shanghai; 200433, China;
7.State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, Beijing; 100101, China;
8.Department of Psychology, University of the Chinese Academy of Sciences, Beijing; 100049, China
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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