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    188宝金博页面版: A survey facial micro-expression recognition

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    内容提示： A survey: facial micro-expression recognitionMadhumita Takalkar 1 & Min Xu 1 & Qiang Wu 1 &Zenon Chaczko 1Received: 29 November 2016 /Revised: 13 June 2017 /Accepted: 17 October 2017 /Published online: 13 November 2017# Springer Science+Business Media, LLC 2017Abstract Facial expression recognition plays a crucial role in a wide range of applications ofpsychotherapy, security systems, marketing, commerce and much more. Detecting a macro-expression, which is a direct representation of an ‘emotion,’ is a r...

    文档格式：PDF | 页数：25 | 浏览次数：30 | 上传日期：2020-03-09 22:44:34 | 文档星级：
    A survey: facial micro-expression recognitionMadhumita Takalkar 1 & Min Xu 1 & Qiang Wu 1 &Zenon Chaczko 1Received: 29 November 2016 /Revised: 13 June 2017 /Accepted: 17 October 2017 /Published online: 13 November 2017# Springer Science+Business Media, LLC 2017Abstract Facial expression recognition plays a crucial role in a wide range of applications ofpsychotherapy, security systems, marketing, commerce and much more. Detecting a macro-expression, which is a direct representation of an ‘emotion,’ is a relatively straight-forwardtask. Playing a pivotal role as macro-expressions, micro-expressions are more accurateindicators of a train of thought or even subtle, passive or involuntary thoughts. Compared tomacro-expressions, identifying micro-expressions is a much more challenging research ques-tion because their time spans are narrowed down to a fraction of a second, and can only bedefined using a broader classification scale. This paper is an all-inclusive survey-cum-analysisof the various micro-expression recognition techniques. We analyze the general framework formicro-expression recognition system by decomposing the pipeline into fundamental compo-nents, namely face detecting, pre-processing, facial feature detection and extraction, datasets,and classification. We discuss the role of these elements and highlight the models and newtrends that are followed in their design. Moreover, we provide an extensive analysis of micro-expression recognition systems by comparing their performance. We also discuss the new deeplearning features that can, in the near future, replace the hand-crafted features for facial micro-expression recognition. This survey has been developed, focusing on the methodologiesapplied, databases used, performance regarding recognition accuracy and comparing these todistil the gaps in the efficiencies, future scope, and research potentials. Through this survey, weintend to look into this problem and develop a comprehensive and efficient recognitionMultimed Tools Appl (2018) 77:19301–19325https://doi.org/10.1007/s11042-017-5317-2* Madhumita Takalkarmadhumita.a.takalkar@student.uts.edu.auMin Xumin.xu@uts.edu.auQiang Wuqiang.wu@uts.edu.auZenon Chaczkozenon.chaczko@uts.edu.au1School of Computing and Communications, Faculty of Engineering and Information Technology,University of Technology Sydney, 15 Broadway, Ultimo, NSW 2007, Australia

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