Image Based Facial Micro-Expression Recognition Using Deep Learning on Small Datasets

Takalkar, MA; Xu, M

Image Based Facial Micro-Expression Recognition Using Deep Learning on Small Datasets

Takalkar, MA

Xu, M

Permalink

Publication Type:: Conference Proceeding
Citation:: DICTA 2017 - 2017 International Conference on Digital Image Computing: Techniques and Applications, 2017, 2017-December pp. 1 - 7
Issue Date:: 2017-12-19

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Accepted manuscript VersionAdobe PDF (624.17 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Takalkar, MA https://orcid.org/0000-0002-3413-0730	en_US
dc.contributor.author	Xu, M https://orcid.org/0000-0001-9581-8849	en_US
dc.date.issued	2017-12-19	en_US
dc.identifier.citation	DICTA 2017 - 2017 International Conference on Digital Image Computing: Techniques and Applications, 2017, 2017-December pp. 1 - 7	en_US
dc.identifier.isbn	9781538628393	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121856
dc.description.abstract	© 2017 IEEE. Facial micro-expression refers to split-second muscle changes in the face, indicating that a person is either consciously or unconsciously suppressing their true emotions and even mental health. Therefore, micro-expression recognition attracts increasing research efforts in both fields of psychology and computer vision. Existing research on micro-expression recognition has mainly used hand-crafted features, for example, Local Binary Pattern-Three Orthogonal Planes (LBP-TOP), Gabor filter and optical flow. Recently, Deep Convolutional neural systems have demonstrated a high degree effectiveness for difficult face recognition tasks. This paper explores the possible use of deep learning for micro-expression recognition. To develop a reliable deep neural network extensive training sets are required with a huge number of labeled image samples. However, micro-expression recognition is a challenging task due to the repressed facial appearance and short duration, which results in the lack of training data. In this paper, we propose to generate extensive training datasets of synthetic images using data augmentation on CASME and CASME II databases. Then, these datasets are combined to tune a satisfactory CNN-based micro-expression recognizer. Experimental results demonstrate the effectiveness of the proposed CNN approach in image based micro- expression recognition and present comparable results with the best-related works.	en_US
dc.relation.ispartof	DICTA 2017 - 2017 International Conference on Digital Image Computing: Techniques and Applications	en_US
dc.relation.isbasedon	10.1109/DICTA.2017.8227443	en_US
dc.title	Image Based Facial Micro-Expression Recognition Using Deep Learning on Small Datasets	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2017-December	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	2017-December	en_US

Abstract:

© 2017 IEEE. Facial micro-expression refers to split-second muscle changes in the face, indicating that a person is either consciously or unconsciously suppressing their true emotions and even mental health. Therefore, micro-expression recognition attracts increasing research efforts in both fields of psychology and computer vision. Existing research on micro-expression recognition has mainly used hand-crafted features, for example, Local Binary Pattern-Three Orthogonal Planes (LBP-TOP), Gabor filter and optical flow. Recently, Deep Convolutional neural systems have demonstrated a high degree effectiveness for difficult face recognition tasks. This paper explores the possible use of deep learning for micro-expression recognition. To develop a reliable deep neural network extensive training sets are required with a huge number of labeled image samples. However, micro-expression recognition is a challenging task due to the repressed facial appearance and short duration, which results in the lack of training data. In this paper, we propose to generate extensive training datasets of synthetic images using data augmentation on CASME and CASME II databases. Then, these datasets are combined to tune a satisfactory CNN-based micro-expression recognizer. Experimental results demonstrate the effectiveness of the proposed CNN approach in image based micro- expression recognition and present comparable results with the best-related works.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/121856