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SMEConvNet: A Convolutional Neural Network for Spotting Spontaneous Facial Micro-Expression from Long Videos
Zhang, Zhihao1; Chen, Tong2; Meng, Hongying3; Liu, Guangyuan1; Fu, Xiaolan4
第一作者Zhang, ZH
2018
发表期刊IEEE Access
通讯作者邮箱c_tong@swu.edu.cn
ISSN2169-3536
文章类型article
产权排序4
摘要Micro-expression is a subtle and involuntary facial expression that may reveal the hidden emotion of human beings. Spotting micro-expression means to locate the moment when the microexpression happens, which is a primary step for micro-expression recognition. Previous work in microexpression expression spotting focus on spotting micro-expression from short video, and with hand-crafted features. In this paper, we present a methodology for spotting micro-expression from long videos. Specifically, a new convolutional neural network named as SMEConvNet (Spotting Micro-Expression Convolutional Network) was designed for extracting features from video clips, which is the first time that deep learning is used in micro-expression spotting. Then a feature matrix processing method was proposed for spotting the apex frame from long video, which uses a sliding window and takes the characteristics of micro-expression into account to search the apex frame. Experimental results demonstrate that the proposed method can achieve better performance than existing state-of-art methods. OAPA
关键词Spotting micro-expression apex frame convolutional neural network deep learning
学科领域Convolution - Neural Networks
学科门类Deep learning
DOI10.1109/ACCESS.2018.2879485
收录类别SCIE ; EI
语种英语
项目简介This work was supported in part by the National Natural Science Foundation of China under Grant 61301297 and Grant 61502398, in part by the National Natural Science Foundation of China (NSFC), and in part by the German Research Foundation (DFG) under project Cross Modal Learning under Grant NSFC 6162113608/DFG TRR-169. 出版商
出版者Institute of Electrical and Electronics Engineers Inc.
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文献类型期刊论文
条目标识符http://ir.psych.ac.cn/handle/311026/27769
专题认知与发展心理学研究室
作者单位1.Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing, Southwest University, Chongqing, 400715, China and Chongqing Key Laboratory of Artificial Intelligence and Service Robot Control Technology, Chongqing, 400715, China.;
2.Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing, Southwest University, Chongqing, 400715, China and Chongqing Key Laboratory of Artificial Intelligence and Service Robot Control Technology, Chongqing, 400715, China and Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China.;
3.Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing, Southwest University, Chongqing, 400715, China and Department of Electronic and Computer Engineering, Brunel University London, UB8 3PH, UK.;
4.Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China and Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China.
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Zhang, Zhihao,Chen, Tong,Meng, Hongying,et al. SMEConvNet: A Convolutional Neural Network for Spotting Spontaneous Facial Micro-Expression from Long Videos[J]. IEEE Access,2018.
APA Zhang, Zhihao,Chen, Tong,Meng, Hongying,Liu, Guangyuan,&Fu, Xiaolan.(2018).SMEConvNet: A Convolutional Neural Network for Spotting Spontaneous Facial Micro-Expression from Long Videos.IEEE Access.
MLA Zhang, Zhihao,et al."SMEConvNet: A Convolutional Neural Network for Spotting Spontaneous Facial Micro-Expression from Long Videos".IEEE Access (2018).
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