Robust Face Recognition via Block Sparse Bayesian Learning
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-11-27
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Face recognition (FR) is an important task in pattern recognition and computer vision.
Sparse representation (SR) has been demonstrated to be a powerful framework for FR.
In general, an SR algorithm treats each face in a training dataset as a basis function and tries to find a sparse representation of a test face under these basis functions.
The sparse representation coefficients then provide a recognition hint.
Early SR algorithms are based on a basic sparse model.
Recently, it has been found that algorithms based on a block sparse model can achieve better recognition rates.
Based on this model, in this study, we use block sparse Bayesian learning (BSBL) to find a sparse representation of a test face for recognition.
BSBL is a recently proposed framework, which has many advantages over existing block-sparse-model-based algorithms.
Experimental results on the Extended Yale B, the AR, and the CMU PIE face databases show that using BSBL can achieve better recognition rates and higher robustness than state-of-the-art algorithms in most cases.
American Psychological Association (APA)
Li, Taiyong& Zhang, Zhilin. 2013. Robust Face Recognition via Block Sparse Bayesian Learning. Mathematical Problems in Engineering،Vol. 2013, no. 2013, pp.1-13.
https://search.emarefa.net/detail/BIM-1010392
Modern Language Association (MLA)
Li, Taiyong& Zhang, Zhilin. Robust Face Recognition via Block Sparse Bayesian Learning. Mathematical Problems in Engineering No. 2013 (2013), pp.1-13.
https://search.emarefa.net/detail/BIM-1010392
American Medical Association (AMA)
Li, Taiyong& Zhang, Zhilin. Robust Face Recognition via Block Sparse Bayesian Learning. Mathematical Problems in Engineering. 2013. Vol. 2013, no. 2013, pp.1-13.
https://search.emarefa.net/detail/BIM-1010392
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1010392