University of Oulu

Thuong-Khanh Tran, Quang-Nhat Vo, Xiaopeng Hong, Xiaobai Li, Guoying Zhao, Micro-expression spotting: A new benchmark, Neurocomputing, Volume 443, 2021, Pages 356-368, ISSN 0925-2312,

Micro-expression spotting : a new benchmark

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Author: Tran, Thuong-Khanh1; Vo, Quang-Nhat1; Hong, Xiaopeng2;
Organizations: 1Center for Machine Vision and Signal Analysis, University of Oulu, Finland
2School of Electronic and Information Engineering, Xi’an Jiaotong University, China
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.5 MB)
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Language: English
Published: Elsevier, 2021
Publish Date: 2021-07-08


Micro-expressions (MEs) are brief and involuntary facial expressions that occur when people are trying to hide their true feelings or conceal their emotions. Based on psychology research, MEs play an important role in understanding genuine emotions, which leads to many potential applications. Therefore, ME analysis has become an attractive topic for various research areas, such as psychology, law enforcement, and psychotherapy. In the computer vision field, the study of MEs can be divided into two main tasks, spotting and recognition, which are used to identify positions of MEs in videos and determine the emotion category of the detected MEs, respectively. Recently, although much research has been done, no fully automatic system for analyzing MEs has yet been constructed on a practical level for two main reasons: most of the research on MEs only focuses on the recognition part, while abandoning the spotting task; current public datasets for ME spotting are not challenging enough to support developing a robust spotting algorithm. The contributions of this paper are threefold: (1) we introduce an extension of the SMIC-E database, namely the SMIC-E-Long database, which is a new challenging benchmark for ME spotting; (2) we suggest a new evaluation protocol that standardizes the comparison of various ME spotting techniques; (3) extensive experiments with handcrafted and deep learning-based approaches on the SMIC-E-Long database are performed for baseline evaluation.

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Series: Neurocomputing
ISSN: 0925-2312
ISSN-E: 1872-8286
ISSN-L: 0925-2312
Volume: 443
Pages: 356 - 368
DOI: 10.1016/j.neucom.2021.02.022
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: This work was supported by Infotech Oulu, Ministry of Education and Culture of Finland for AI forum project, the Academy of Finland for project MiGA (grant 316765), ICT 2023 project (grant 328115) and Academy of Finland postdoctoral Project 6+E (grant 323287). As well, the authors wish to acknowledge CSC IT Center for Science, Finland, for computational resources.
Academy of Finland Grant Number: 316765
Detailed Information: 316765 (Academy of Finland Funding decision)
328115 (Academy of Finland Funding decision)
323287 (Academy of Finland Funding decision)
Copyright information: © 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (