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Conference Paper: Robust and practical face recognition via structured sparsity
Title | Robust and practical face recognition via structured sparsity |
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Authors | |
Issue Date | 2012 |
Citation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, v. 7575 LNCS, n. PART 4, p. 331-344 How to Cite? |
Abstract | Sparse representation based classification (SRC) methods have recently drawn much attention in face recognition, due to their good performance and robustness against misalignment, illumination variation, and occlusion. They assume the errors caused by image variations can be modeled as pixel-wisely sparse. However, in many practical scenarios these errors are not truly pixel-wisely sparse but rather sparsely distributed with structures, i.e., they constitute contiguous regions distributed at different face positions. In this paper, we introduce a class of structured sparsity-inducing norms into the SRC framework, to model various corruptions in face images caused by misalignment, shadow (due to illumination change), and occlusion. For practical face recognition, we develop an automatic face alignment method based on minimizing the structured sparsity norm. Experiments on benchmark face datasets show improved performance over SRC and other alternative methods. © 2012 Springer-Verlag. |
Persistent Identifier | http://hdl.handle.net/10722/326912 |
ISSN | 2023 SCImago Journal Rankings: 0.606 |
DC Field | Value | Language |
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dc.contributor.author | Jia, Kui | - |
dc.contributor.author | Chan, Tsung Han | - |
dc.contributor.author | Ma, Yi | - |
dc.date.accessioned | 2023-03-31T05:27:26Z | - |
dc.date.available | 2023-03-31T05:27:26Z | - |
dc.date.issued | 2012 | - |
dc.identifier.citation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, v. 7575 LNCS, n. PART 4, p. 331-344 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/10722/326912 | - |
dc.description.abstract | Sparse representation based classification (SRC) methods have recently drawn much attention in face recognition, due to their good performance and robustness against misalignment, illumination variation, and occlusion. They assume the errors caused by image variations can be modeled as pixel-wisely sparse. However, in many practical scenarios these errors are not truly pixel-wisely sparse but rather sparsely distributed with structures, i.e., they constitute contiguous regions distributed at different face positions. In this paper, we introduce a class of structured sparsity-inducing norms into the SRC framework, to model various corruptions in face images caused by misalignment, shadow (due to illumination change), and occlusion. For practical face recognition, we develop an automatic face alignment method based on minimizing the structured sparsity norm. Experiments on benchmark face datasets show improved performance over SRC and other alternative methods. © 2012 Springer-Verlag. | - |
dc.language | eng | - |
dc.relation.ispartof | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.title | Robust and practical face recognition via structured sparsity | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/978-3-642-33765-9_24 | - |
dc.identifier.scopus | eid_2-s2.0-84867850232 | - |
dc.identifier.volume | 7575 LNCS | - |
dc.identifier.issue | PART 4 | - |
dc.identifier.spage | 331 | - |
dc.identifier.epage | 344 | - |
dc.identifier.eissn | 1611-3349 | - |