Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/69933
Title: Gabor feature based sparse representation for face recognition with gabor occlusion dictionary
Authors: Yang, M
Zhang, L 
Issue Date: 2010
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2010, v. 6316, p. 448-461 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: By coding the input testing image as a sparse linear combination of the training samples via l1-norm minimization, sparse representation based classification (SRC) has been recently successfully used for face recognition (FR). Particularly, by introducing an identity occlusion dictionary to sparsely code the occluded portions in face images, SRC can lead to robust FR results against occlusion. However, the large amount of atoms in the occlusion dictionary makes the sparse coding computationally very expensive. In this paper, the image Gabor-features are used for SRC. The use of Gabor kernels makes the occlusion dictionary compressible, and a Gabor occlusion dictionary computing algorithm is then presented. The number of atoms is significantly reduced in the computed Gabor occlusion dictionary, which greatly reduces the computational cost in coding the occluded face images while improving greatly the SRC accuracy. Experiments on representative face databases with variations of lighting, expression, pose and occlusion demonstrated the effectiveness of the proposed Gabor-feature based SRC (GSRC) scheme.
Description: 11th European Conference on Computer Vision, Heraklion, Crete, Greece, 5-11 Sep. 2010
URI: http://hdl.handle.net/10397/69933
ISBN: 978-3-642-15566-6
978-3-642-15567-3
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-642-15567-3_33
Appears in Collections:Conference Paper

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

Google ScholarTM

Check

Altmetric



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