Sliding window based micro-expression spotting : a benchmark

Tran, Thuong-Khanh; Hong, Xiaopeng; Zhao, Guoying (2017-11-23)
 
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URL:
https://doi.org/10.1007/978-3-319-70353-4_46

Tran, Thuong-Khanh
Hong, Xiaopeng
Zhao, Guoying
Springer Nature
23.11.2017

Tran TK., Hong X., Zhao G. (2017) Sliding Window Based Micro-expression Spotting: A Benchmark. In: Blanc-Talon J., Penne R., Philips W., Popescu D., Scheunders P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2017. Lecture Notes in Computer Science, vol 10617. Springer, Cham

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© Springer International Publishing AG 2017. Published in this repository with the kind permission of the publisher.
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doi:https://doi.org/10.1007/978-3-319-70353-4_46
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https://urn.fi/URN:NBN:fi-fe2017120155190
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Abstract

Micro-expressions are very rapid and involuntary facial expressions, which indicate the suppressed or concealed emotions and can lead to many potential applications. Recently, research in micro-expression spotting obtains increasing attention. By investigating existing methods, we realize that evaluation standards of micro-expression spotting methods are highly desired. To address this issue, we construct a benchmark for fairer and better performance evaluation of micro-expression spotting approaches. Firstly, we propose a sliding window based multi-scale evaluation standard with a series of protocols. Secondly, baseline results of popular features are provided. Finally, we also raise the concerns of taking advantages of machine learning techniques.

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