Kernel sparse representation with local patterns for face recognition

Cuicui Kang; Shengcai Liao; Shiming Xiang; Chunhong Pan

doi:10.1109/ICIP.2011.6116296

Kernel sparse representation with local patterns for face recognition

Cuicui Kang, Shengcai Liao, Shiming Xiang, Chunhong Pan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

37 Citations (Scopus)

Abstract

In this paper we propose a novel kernel sparse representation classification (SRC) framework and utilize the local binary pattern (LBP) descriptor in this framework for robust face recognition. First we develop a kernel coordinate descent (KCD) algorithm for 11 minimization in the kernel space, which is based on the covariance update technique. Then we extract LBP descriptors from each image and apply two types of kernels (χ ² distance based and Hamming distance based) with the proposed KCD algorithm under the SRC framework for face recognition. Experiments on both the Extended Yale B and the PIE face databases show that the proposed method is more robust against noise, occlusion, and illumination variations, even with small number of training samples.

Original language	English
Title of host publication	ICIP 2011
Subtitle of host publication	2011 18th IEEE International Conference on Image Processing
Pages	3009-3012
Number of pages	4
DOIs	https://doi.org/10.1109/ICIP.2011.6116296
Publication status	Published - 2011
Externally published	Yes
Event	2011 18th IEEE International Conference on Image Processing, ICIP 2011 - Brussels, Belgium Duration: Sept 11 2011 → Sept 14 2011

Publication series

Name	Proceedings - International Conference on Image Processing, ICIP
ISSN (Print)	1522-4880

Conference

Conference	2011 18th IEEE International Conference on Image Processing, ICIP 2011
Country/Territory	Belgium
City	Brussels
Period	9/11/11 → 9/14/11

Keywords

face recognition
kernel
local binary pattern
sparse representation

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition
Signal Processing

Access to Document

10.1109/ICIP.2011.6116296

Cite this

Kernel sparse representation with local patterns for face recognition. / Kang, Cuicui; Liao, Shengcai; Xiang, Shiming et al.
ICIP 2011: 2011 18th IEEE International Conference on Image Processing. 2011. p. 3009-3012 6116296 (Proceedings - International Conference on Image Processing, ICIP).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kang, C, Liao, S, Xiang, S & Pan, C 2011, Kernel sparse representation with local patterns for face recognition. in ICIP 2011: 2011 18th IEEE International Conference on Image Processing., 6116296, Proceedings - International Conference on Image Processing, ICIP, pp. 3009-3012, 2011 18th IEEE International Conference on Image Processing, ICIP 2011, Brussels, Belgium, 9/11/11. https://doi.org/10.1109/ICIP.2011.6116296

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AB - In this paper we propose a novel kernel sparse representation classification (SRC) framework and utilize the local binary pattern (LBP) descriptor in this framework for robust face recognition. First we develop a kernel coordinate descent (KCD) algorithm for 11 minimization in the kernel space, which is based on the covariance update technique. Then we extract LBP descriptors from each image and apply two types of kernels (χ 2 distance based and Hamming distance based) with the proposed KCD algorithm under the SRC framework for face recognition. Experiments on both the Extended Yale B and the PIE face databases show that the proposed method is more robust against noise, occlusion, and illumination variations, even with small number of training samples.

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Kernel sparse representation with local patterns for face recognition

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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