Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/21171
Title: Facial expression recognition on multiple manifolds
Authors: Xiao, R
Zhao, Q
Zhang, D 
Shi, P
Keywords: Classification
Facial expression recognition
Multiple manifolds
Issue Date: 2011
Publisher: Elsevier
Source: Pattern recognition, 2011, v. 44, no. 1, p. 107-116 How to cite?
Journal: Pattern recognition 
Abstract: Manifold learning has been successfully applied to facial expression recognition by modeling different expressions as a smooth manifold embedded in a high dimensional space. However, the assumption of single manifold is still arguable and therefore does not necessarily guarantee the best classification accuracy. In this paper, a generalized framework for modeling and recognizing facial expressions on multiple manifolds is presented which assumes that different expressions may reside on different manifolds of possibly different dimensionalities. The intrinsic features of each expression are firstly learned separately and the genetic algorithm (GA) is then employed to obtain the nearly optimal dimensionality of each expression manifold from the classification viewpoint. Classification is performed under a newly defined criterion that is based on the minimum reconstruction error on manifolds. Extensive experiments on both the CohnKanade and Feedtum databases show the effectiveness of the proposed multiple manifold based approach.
URI: http://hdl.handle.net/10397/21171
ISSN: 0031-3203
EISSN: 1873-5142
DOI: 10.1016/j.patcog.2010.07.017
Appears in Collections:Journal/Magazine Article

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

SCOPUSTM   
Citations

72
Last Week
1
Last month
1
Citations as of Aug 14, 2017

WEB OF SCIENCETM
Citations

55
Last Week
0
Last month
0
Citations as of Aug 21, 2017

Page view(s)

40
Last Week
1
Last month
Checked on Aug 20, 2017

Google ScholarTM

Check

Altmetric



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