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- Publisher Website: 10.1109/TIP.2013.2262292
- Scopus: eid_2-s2.0-84879044578
- PMID: 23674456
- WOS: WOS:000321926600026
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Article: Fast ℓ1 -minimization algorithms for robust face recognition
Title | Fast ℓ<inf>1</inf>-minimization algorithms for robust face recognition |
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Authors | |
Keywords | ℓ -minimization 1 augmented Lagrangian methods face recognition |
Issue Date | 2013 |
Citation | IEEE Transactions on Image Processing, 2013, v. 22, n. 8, p. 3234-3246 How to Cite? |
Abstract | ℓ1-minimization refers to finding the minimum ℓ1-norm solution to an underdetermined linear system b=Ax. Under certain conditions as described in compressive sensing theory, the minimum ℓ1-norm solution is also the sparsest solution. In this paper, we study the speed and scalability of its algorithms. In particular, we focus on the numerical implementation of a sparsity-based classification framework in robust face recognition, where sparse representation is sought to recover human identities from high-dimensional facial images that may be corrupted by illumination, facial disguise, and pose variation. Although the underlying numerical problem is a linear program, traditional algorithms are known to suffer poor scalability for large-scale applications. We investigate a new solution based on a classical convex optimization framework, known as augmented Lagrangian methods. We conduct extensive experiments to validate and compare its performance against several popular ℓ1-minimization solvers, including interior-point method, Homotopy, FISTA, SESOP-PCD, approximate message passing, and TFOCS. To aid peer evaluation, the code for all the algorithms has been made publicly available. © 1992-2012 IEEE. |
Persistent Identifier | http://hdl.handle.net/10722/326939 |
ISSN | 2023 Impact Factor: 10.8 2023 SCImago Journal Rankings: 3.556 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Yang, Allen Y. | - |
dc.contributor.author | Zhou, Zihan | - |
dc.contributor.author | Balasubramanian, Arvind Ganesh | - |
dc.contributor.author | Sastry, S. Shankar | - |
dc.contributor.author | Ma, Yi | - |
dc.date.accessioned | 2023-03-31T05:27:38Z | - |
dc.date.available | 2023-03-31T05:27:38Z | - |
dc.date.issued | 2013 | - |
dc.identifier.citation | IEEE Transactions on Image Processing, 2013, v. 22, n. 8, p. 3234-3246 | - |
dc.identifier.issn | 1057-7149 | - |
dc.identifier.uri | http://hdl.handle.net/10722/326939 | - |
dc.description.abstract | ℓ1-minimization refers to finding the minimum ℓ1-norm solution to an underdetermined linear system b=Ax. Under certain conditions as described in compressive sensing theory, the minimum ℓ1-norm solution is also the sparsest solution. In this paper, we study the speed and scalability of its algorithms. In particular, we focus on the numerical implementation of a sparsity-based classification framework in robust face recognition, where sparse representation is sought to recover human identities from high-dimensional facial images that may be corrupted by illumination, facial disguise, and pose variation. Although the underlying numerical problem is a linear program, traditional algorithms are known to suffer poor scalability for large-scale applications. We investigate a new solution based on a classical convex optimization framework, known as augmented Lagrangian methods. We conduct extensive experiments to validate and compare its performance against several popular ℓ1-minimization solvers, including interior-point method, Homotopy, FISTA, SESOP-PCD, approximate message passing, and TFOCS. To aid peer evaluation, the code for all the algorithms has been made publicly available. © 1992-2012 IEEE. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Transactions on Image Processing | - |
dc.subject | ℓ -minimization 1 | - |
dc.subject | augmented Lagrangian methods | - |
dc.subject | face recognition | - |
dc.title | Fast ℓ<inf>1</inf>-minimization algorithms for robust face recognition | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TIP.2013.2262292 | - |
dc.identifier.pmid | 23674456 | - |
dc.identifier.scopus | eid_2-s2.0-84879044578 | - |
dc.identifier.volume | 22 | - |
dc.identifier.issue | 8 | - |
dc.identifier.spage | 3234 | - |
dc.identifier.epage | 3246 | - |
dc.identifier.isi | WOS:000321926600026 | - |