Enhancing sparsity via ℓp (0<p<1) minimization for robust face recognition

Sparse representation has received an increasing amount of interest in recent years. By representing the testing image as a sparse linear combination of the training samples, sparse representation based classification (SRC) has been successfully applied in face recognition. In SRC, the ℓ1 minimizati...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2013-01, Vol.99, p.592-602
Main Authors: Guo, Song, Wang, Zhan, Ruan, Qiuqi
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Face recognition
Information, signal and communications theory
Pattern recognition. Digital image processing. Computational geometry
Signal and communications theory
Signal, noise
Sparse representation
Sparsity ratio
Telecommunications and information theory
ℓ1 minimization
ℓp minimization
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Summary:Sparse representation has received an increasing amount of interest in recent years. By representing the testing image as a sparse linear combination of the training samples, sparse representation based classification (SRC) has been successfully applied in face recognition. In SRC, the ℓ1 minimization instead of the ℓ0 minimization is used to seek for the sparse solution for its computational convenience and efficiency. However, ℓ1 minimization does not always yield sufficiently sparse solution in many practical applications. In this paper, we propose a novel SRC method, namely the ℓp (0
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2012.05.028