University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Local binary patterns for multi-view facial expression recognition

Moore, S and Bowden, R (2011) Local binary patterns for multi-view facial expression recognition Computer Vision and Image Understanding, 115 (4). pp. 541-558.

[img] PDF
mooreCVIU2011.pdf
Restricted to Repository staff only
Available under License : See the attached licence file.

Download (3MB)
[img] PDF (licence)
SRI_deposit_agreement.pdf
Restricted to Repository staff only

Download (33kB)

Abstract

Research into facial expression recognition has predominantly been applied to face images at frontal view only. Some attempts have been made to produce pose invariant facial expression classifiers. However, most of these attempts have only considered yaw variations of up to 45°, where all of the face is visible. Little work has been carried out to investigate the intrinsic potential of different poses for facial expression recognition. This is largely due to the databases available, which typically capture frontal view face images only. Recent databases, BU3DFE and multi-pie, allows empirical investigation of facialexpressionrecognition for different viewing angles. A sequential 2 stage approach is taken for pose classification and view dependent facialexpression classification to investigate the effects of yaw variations from frontal to profile views. Local binary patterns (LBPs) and variations of LBPs as texture descriptors are investigated. Such features allow investigation of the influence of orientation and multi-resolution analysis for multi-view facial expression recognition. The influence of pose on different facial expressions is investigated. Others factors are investigated including resolution and construction of global and local feature vectors. An appearance based approach is adopted by dividing images into sub-blocks coarsely aligned over the face. Feature vectors contain concatenated feature histograms built from each sub-block. Multi-class support vector machines are adopted to learn pose and pose dependent facial expression classifiers.

Item Type: Article
Authors :
AuthorsEmailORCID
Moore, SUNSPECIFIEDUNSPECIFIED
Bowden, RUNSPECIFIEDUNSPECIFIED
Date : 2011
Identification Number : https://doi.org/10.1016/j.cviu.2010.12.001
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 14:42
Last Modified : 28 Mar 2017 14:42
URI: http://epubs.surrey.ac.uk/id/eprint/531454

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800