| 一种视频微表情检测的改进光流算法 |
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| 作者姓名： | 李秋宇 张玉明 杨福猛 詹 曙 |
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| 作者单位： | 1. 合肥工业大学计算机与信息学院，安徽合肥 230009；
2. 芜湖职业技术学院电气工程学院，安徽芜湖 241000；
3. 安徽信息工程学院，安徽芜湖 241000 |
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| 基金项目： | 国家自然科学基金面上项目(61371156) |
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| 摘 要： | 微表情是人们在试图隐藏自己真实情感时表现出的不受自主神经控制、持续时间
短暂，强度十分微弱的面部表情。由于微表情与谎言识别有着密切的联系，其公共安全、侦查
讯问、临床医学等领域有很大的应用前景。针对人为识别微表情十分困难的问题，提出一种基
于Horn-Schunck (HS)光流法改进并应用于微表情自动检测的方法。使用预条件Gauss-Seidel 迭
代方法改进了HS 光流法，加快了收敛速度。通过在自发微表情数据库CASME 中进行实验，
该验证方法在微表情检测中有很好的效果。
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| 关 键 词： | 微表情检测 光流法 预条件迭代 |
An Improved Optical Flow Algorithm for Micro Expression Detection in the Video Sequence |
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| Authors: | LI Qiuyu ZHANG Yuming YANG Fumeng ZHAN Shu |
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| Affiliation: | 1. School of Computer and Information, Hefei University of Technology, Hefei Anhui 230009, China;
2. School of Electrical Engineering, Wuhu Institute of Technology, Wuhu Anhui 241000, China;
3. Anhui Institute of Information Technology, Wuhu Anhui 241000, China |
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| Abstract: | Micro-expression is a kind of short-duration subtle expression which is not controlled by
the autonomic nervous system. Micro-expression appears when a person is attempting to conceal his
true emotion. Micro-expression detection boasts great application prospects in many fields, such as
public security, investigation and interrogation as well as clinical medicine due to its close
relationship with lie detection. Automatic detection of micro-expressions has come to the fore in
research, because it is of great difficulty to artificially identify micro-expression . This paper proposes
an improved algorithm based on the Horn-Schunck (HS) optical flow for automatic micro-expression
detection. In this study, the pre-conditioned Gauss-Seidel iterative method is employed to improve the
HS optical flow method, which accelerates the convergence rate. Experiments in the spontaneous
micro-expression database CASME show that the propounded method exerts an excellent effect on
the detection of micro-expression. |
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| Keywords: | micro-expression detection optical flow preconditioned iteration |
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