Institutional Repository of Key Laboratory of Behavioral Science, CAS
Simple but Effective In-the-wild Micro-Expression Spotting Based on Head Pose Segmentation | |
Yang, Xingpeng1; Yang, Henian1; Li, Jingting2; Wang, Su-Jing2 | |
2023 | |
通讯作者邮箱 | wang, su-jing |
会议名称 | Proceedings of the 3rd Workshop on Facial Micro-Expression: Advanced Techniques for Multi-Modal Facial Expression Analysis |
会议录名称 | FME 2023 - Proceedings of the 3rd Workshop on Facial Micro-Expression: Advanced Techniques for Multi-Modal Facial Expression Analysis |
页码 | 2023, Pages 9-16 |
会议日期 | 2023 |
会议地点 | 不详 |
摘要 | Micro-expressions may occur in high-stake situations when people attempt to conceal or suppress their true feelings. Nowadays, intelligent micro-expression analysis has long been focused on videos captured under constrained laboratory conditions. This is due to the relatively small number of publicly available datasets. Moreover, micro-expression characteristics are subtle and brief, and thus very susceptible to interference from external factors and difficult to capture. In particular, head movement is unavoidable in unconstrained scenarios, making micro-expression spotting highly challenging. This paper proposes a simple yet effective method for avoiding the interference of head movement on micro-expression spotting in natural scenarios by considering three-dimensional space. In particular, based on the head pose, which can be mapped to two-dimensional vectors (translations and rotations) for representation, long and complex videos could be divided into short video segments that basically exclude head movement interference. Following that, segmented micro-expression spotting is realized based on an effective short-segment-based micro-expression spotting algorithm. Experimental results on in-the-wild databases demonstrate the effectiveness of our proposed method in avoiding head movement interference. Additionally, due to the simplicity of this method, it creates opportunities for spotting micro-expressions in real-world scenarios, possibly even in real-time. Furthermore, it helps alleviate the small sample size problem in micro-expression analysis by boosting the spotting performance in massive unlabeled videos. |
关键词 | Micro-expressions spotting In-the-wild database Temporal seg- mentation Head pose estimation |
DOI | 10.1145/3607829.3616445 |
收录类别 | EI |
引用统计 | |
文献类型 | 会议论文 |
条目标识符 | http://ir.psych.ac.cn/handle/311026/46355 |
专题 | 中国科学院行为科学重点实验室 |
作者单位 | 1.Cas Key Laboratory of Behavioral Science, Institute of Psychology, School of Computer, Jiangsu University of Science and Technology, Beijing, China 2.Cas Key Laboratory of Behavioral Science, Institute of Psychology, Department of Psychology, University of the Chinese Academy of Sciences, Beijing, China |
推荐引用方式 GB/T 7714 | Yang, Xingpeng,Yang, Henian,Li, Jingting,et al. Simple but Effective In-the-wild Micro-Expression Spotting Based on Head Pose Segmentation[C],2023:2023, Pages 9-16. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Simple but Effective(1722KB) | 会议论文 | 限制开放 | CC BY-NC-SA | 请求全文 |
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