Institutional Repository of Key Laboratory of Behavioral Science, CAS
| 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 Author | Liu, Yong-Jin(liuyongjin@tsinghua.edu.cn) |
| Abstract | 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. |
| Keyword | Micro-expression Deep learning Transferring learning Convolutional neural network |
| 2018-10-27 | |
| Language | 英语 |
| DOI | 10.1016/j.neucom.2018.05.107 |
| Source Publication | NEUROCOMPUTING
 |
| ISSN | 0925-2312 |
| Volume | 312Pages:251-262 |
| Indexed By | SCI |
| Funding Project | 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] |
| Publisher | ELSEVIER SCIENCE BV |
| WOS Keyword | FACIAL EXPRESSIONS ; SCHIZOPHRENIA ; REMEDIATION ; DECEPTION ; LIES |
| WOS Research Area | Computer Science |
| WOS Subject | Computer Science, Artificial Intelligence |
| WOS ID | WOS:000438668100022 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.psych.ac.cn/handle/311026/27762 |
| Collection | 中国科学院行为科学重点实验室 |
| Corresponding Author | Liu, Yong-Jin |
| Affiliation | 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 |
| First Author Affilication | Key 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. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| Micro-expression rec（2521KB） | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | Application Full Text | |
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment