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Effective recognition of facial micro-expressions with video motion magnification

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posted on 11.11.2016 by Yandan Wang, John See, Yee-Hui Oh, Raphael C.-W. Phan, Yogachandran Rahulamathavan, Huo-Chong Ling, Su-Wei Tan, Xujie Li
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.

Funding

This work is supported by the TM Grant under project UbeAware and 2beAware,and Zhejiang Provincial Natural Science Foundation of China (Grant Nos. LQ14F020006).

History

School

  • Loughborough University London

Published in

Multimedia Tools and Applications

Citation

WANG, Y. ... et al, 2016. Effective recognition of facial micro-expressions with video motion magnification. Multimedia Tools and Applications, 76 (20), pp. 21665–21690.

Publisher

© Springer

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

16/10/2016

Publication date

2016

Notes

The final publication is available at link.springer.com via http://dx.doi.org/10.1007/s11042-016-4079-6.

ISSN

1380-7501

eISSN

1573-7721

Language

en

Exports