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Robust linear representation via exploiting structure prior
Wang, Dong; He, Ran; Wang, Liang; Tan, Tieniu
Source PublicationPATTERN RECOGNITION
2018-04-01
Volume76Issue:NAPages:560-568
SubtypeArticle
AbstractOver the past few years, linear representation models have seen a lot of successful applications such as face recognition in computer vision. In the context of face recognition, occlusion is a key factor that often curbs the performance of practical face recognition systems. In this paper, we propose to alleviate such negative influence of the occlusion noises by explicitly encoding the spatial continuity prior of the occlusion. Given the fact that many real-world occlusions such as sunglasses and scarves are contiguous, taking such prior into account can help build a more accurate model and achieve higher recognition rates. Besides, a general framework has also been proposed in which many off-the-shelf linear representation models can be nicely incorporated. And the minimization objectives of all these models can be solved via the same Half-Quadratic optimization procedure. Therefore the robustness of these models to occlusions can be comprehensively evaluated on a fair platform. Extensive experiments on the AR and Extended Yale B face databases corroborate that the proposed algorithms can improve the model robustness to contiguous occlusions. (C) 2017 Elsevier Ltd. All rights reserved.
KeywordLinear Representation Half-quadratic Optimization Occlusion Prior
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.patcog.2017.08.027
WOS KeywordFACE RECOGNITION ; SPARSE REPRESENTATION ; SUBSPACES
Indexed BySCI
Language英语
Funding OrganizationNational Basic Research Program of China(2012CB316302)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000424853800041
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21078
Collection智能感知与计算研究中心
AffiliationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Ctr Res Intelligent Percept & Comp, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Wang, Dong,He, Ran,Wang, Liang,et al. Robust linear representation via exploiting structure prior[J]. PATTERN RECOGNITION,2018,76(NA):560-568.
APA Wang, Dong,He, Ran,Wang, Liang,&Tan, Tieniu.(2018).Robust linear representation via exploiting structure prior.PATTERN RECOGNITION,76(NA),560-568.
MLA Wang, Dong,et al."Robust linear representation via exploiting structure prior".PATTERN RECOGNITION 76.NA(2018):560-568.
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