Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/55572
Title: Dynamic texture and geometry features for facial expression recognition in video
Authors: Chen, J
Chen, Z
Chi, Z 
Fu, H
Keywords: Facial expression recognition
Geometry features
Multiple kernel learning
Texture
Issue Date: 2015
Publisher: IEEE Computer Society
Source: Proceedings - International Conference on Image Processing, ICIP, 27-30 September 2015, 7351752, p. 4967-4971 How to cite?
Abstract: Facial expression recognition in video has attracted growing attention recently. In this paper, we propose to handle this problem with dynamic appearance and geometric features. We propose a new feature descriptor called HOG from Three Orthogonal Planes (HOG-TOP) to represent dynamic features. In addition, we introduce two types of geometry features to represent the facial rigid changes and non-rigid changes, respectively. Multiple Kernel Learning (MKL) is applied to find an optimal combination of two types of features. And finally a Support Vector Machine (SVM) with multiple kernels is trained for the facial expression classification. Extensive experiments conducted on the extended Cohn-Kanade dataset show that our method can achieve a competitive performance compared with the other state-of-the-art methods.
URI: http://hdl.handle.net/10397/55572
ISBN: 9781479983391
ISSN: 1522-4880
DOI: 10.1109/ICIP.2015.7351752
Appears in Collections:Conference Paper

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

6
Last Week
1
Last month
Citations as of Aug 12, 2018

WEB OF SCIENCETM
Citations

3
Last Week
1
Last month
Citations as of Aug 17, 2018

Page view(s)

34
Last Week
1
Last month
Citations as of Aug 13, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.