| SMEConvNet: A Convolutional Neural Network for Spotting Spontaneous Facial Micro-Expression From Long Videos | |
Zhang,Zhihao1,2; Chen,Tong1,2,3; Meng,Hongying1,4; Liu,Guangyuan1,2; Fu,Xiaolan3,5
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| 第一作者 | Zhang, Zhihao |
| 通讯作者 | Chen, Tong(c_tong@swu.edu.cn) |
| 通讯作者邮箱 | c_tong@swu.edu.cn |
| 心理所单位排序 | 3 |
| 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 micro-expression happens, which is a primary step for micro-expression recognition. Previous work in micro-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 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 a better performance than the existing state-of-art methods. |
| 关键词 | Spotting micro-expression apex frame convolutional neural network deep learning |
| 2018 | |
| 语种 | 英语 |
| DOI | 10.1109/ACCESS.2018.2879485 |
| 发表期刊 | IEEE ACCESS
 |
| ISSN | 2169-3536 |
| 卷号 | 6页码:71143-71151 |
| URL | 查看原文 |
| 收录类别 | SCI |
| 资助项目 | German Research Foundation (DFG)[NSFC 6162113608/DFG TRR-169] ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China[61502398] ; National Natural Science Foundation of China[61301297] ; German Research Foundation (DFG)[NSFC 6162113608/DFG TRR-169] ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China[61502398] ; National Natural Science Foundation of China[61301297] ; National Natural Science Foundation of China[61301297] ; National Natural Science Foundation of China[61502398] ; National Natural Science Foundation of China (NSFC) ; German Research Foundation (DFG)[NSFC 6162113608/DFG TRR-169] |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS关键词 | RECOGNITION |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| WOS类目 | Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications |
| WOS记录号 | WOS:000453304600001 |
| WOS分区 | Q1 |
| 取样对象 | 人类 |
| 性别 | 男 ; 女 |
| 年龄组 | 青年（18岁-29岁） |
| 被试数量 | 26 |
| 测试或任务 | spotting micro-expression |
| 因变量指标 | apex frame |
| 统计方法 | convolutional neural network;deep learning technique |
| 资助机构 | National Natural Science Foundation of China ; National Natural Science Foundation of China (NSFC) ; German Research Foundation (DFG) |
| 引用统计 | |
| 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.Southwest Univ, Chongqing Key Lab Nonlinear Circuit & Intelligent, Chongqing 400715, Peoples R China; 2.Chongqing Key Lab Artificial Intelligence & Serv, Chongqing 400715, Peoples R China; 3.Chinese Acad Sci, Inst Psychol, Beijing 100101, Peoples R China; 4.Brunel Univ London, Dept Elect & Comp Engn, London UB8 3PH, England; 5.Univ Chinese Acad Sci, Dept Psychol, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | 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,2018,6:71143-71151. |
| 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,6,71143-71151. |
| MLA | Zhang,Zhihao,et al."SMEConvNet: A Convolutional Neural Network for Spotting Spontaneous Facial Micro-Expression From Long Videos".IEEE ACCESS 6(2018):71143-71151. |
| 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 | ||
| SMEConvNet_ A Convol（7031KB） | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 请求全文 | |
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