University of Oulu

Tran, T., Hong, X., Zhao, G. (2017) Sliding Window Based Micro-expression Spotting: A Benchmark. Lecture Notes in Artificial Intelligence, (), 542-553. doi:10.1007/978-3-319-70353-4_46

Sliding window based micro-expression spotting : a benchmark

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Author: Tran, Thuong-Khanh1; Hong, Xiaopeng1; Zhao, Guoying1
Organizations: 1Center for Machine Vision and Signal Analysis, University of Oulu
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.2 MB)
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Language: English
Published: Springer Nature, 2017
Publish Date: 2017-12-01


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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Series: Lecture notes in computer science
ISSN: 0302-9743
ISSN-E: 1611-3349
ISSN-L: 0302-9743
ISBN: 978-3-319-70353-4
ISBN Print: 978-3-319-70352-7
Issue: 10617
DOI: 10.1007/978-3-319-70353-4_46
Host publication editor: Blanc-Talon, Jacques
Penne, Rudi
Philips, Wilfried
Popescu, Dan
Scheunders, Paul
Conference: Advanced Concepts for Intelligent Vision Systems : 18th International Conference
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Copyright information: © Springer International Publishing AG 2017. Published in this repository with the kind permission of the publisher.