University of Oulu

M. H. Yap, J. See, X. Hong and S. Wang, "Facial Micro-Expressions Grand Challenge 2018 Summary," 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), Xi’an, 2018, pp. 675-678. doi: 10.1109/FG.2018.00106

Facial micro-expressions grand challenge 2018 summary

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Author: Yap, Moi Hoon1; See, John2; Hon, Xiaopeng3;
Organizations: 1School of Computing, Mathematics and Digital Technology, Manchester Metropolitan University, UK
2Faculty of Computing and Informatics, Multimedia University, Malaysia
3Center for Machine Vision and Signal Analysis, University of Oulu, Finland
4CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.9 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2018
Publish Date: 2019-08-07


This paper summarises the Facial Micro-Expression Grand Challenge (MEGC 2018) held in conjunction with the 13th IEEE Conference on Automatic Face and Gesture Recognition (FG) 2018. In this workshop, we aim to stimulate new ideas and techniques for facial micro-expression analysis by proposing a new cross-database challenge. Two state-of-the-art datasets, CASME II and SAMM, are used to validate the performance of existing and new algorithms. Also, the challenge advocates the recognition of micro-expressions based on AU-centric objective classes rather than emotional classes. We present a summary and analysis of the baseline results using LBP-TOP, HOOF and 3DHOG, together with results from the challenge submissions.

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ISBN: 978-1-5386-2335-0
ISBN Print: 978-1-5386-2336-7
Pages: 675 - 678
DOI: 10.1109/FG.2018.00106
Host publication: 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018
Conference: IEEE International Conference on Automatic Face and Gesture Recognition
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Funding: The chairs would like to thank their funders: National Natural Science Foundation of China 61772511, 61472138, 61572205), The UK Royal Society Industry Fellowship (IF160006), MOHE Malaysia Grant No. FRGS/1/2016/ICT02/MMU/02/2, Shanghai ’The Belt and Road’ Young Scholar Exchange Grant (17510740100), Academic of Finland, Tekes, and Infotech Oulu.
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