Citation

Wang, Yandan and See, John Su Yang and Oh, Yee Hui and Phan, Raphael Chung Wei and Rahulamathavan, Yogachandran and Ling, Huo Chong and Tan, Su Wei and Li, Xujie (2017) Effective recognition of facial micro-expressions with video motion magnification. Multimedia Tools and Applications, 76 (20). pp. 21665-21690. ISSN 1380-7501; eISSN: 1573-7721

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Abstract

Facial expression recognition has been intensively studied for decades, notably by the psychology community and more recently the pattern recognition community. What is more challenging, and the subject of more recent research, is the problem of recognizing subtle emotions exhibited by so-called micro-expressions. Recognizing a micro-expression is substantially more challenging than conventional expression recognition because these micro-expressions are only temporally exhibited in a fraction of a second and involve minute spatial changes. Until now, work in this field is at a nascent stage, with only a few existing micro-expression databases and methods. In this article, we propose a new micro-expression recognition approach based on the Eulerian motion magnification technique, which could reveal the hidden information and accentuate the subtle changes in micro-expression motion. Validation of our proposal was done on the recently proposed CASME II dataset in comparison with baseline and state-of-the-art methods. We achieve a good recognition accuracy of up to 75.30 % by using leave-one-out cross validation evaluation protocol. Extensive experiments on various factors at play further demonstrate the effectiveness of our proposed approach.

Item Type:	Article
Uncontrolled Keywords:	Micro-expressions, Motion magnification, EVMCASME II, Local binary patterns
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Computing and Informatics (FCI) Faculty of Engineering (FOE)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	12 Dec 2017 13:05
Last Modified:	21 Apr 2021 17:34
URII:	http://shdl.mmu.edu.my/id/eprint/6609

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