Modular weighted global sparse representation for robust face recognition
This work proposes a novel framework of robust face recognition based on the sparse representation. Image is first divided into modules and each module is processed separately to determine its reliability. A reconstructed image from the modules weighted by their reliability is formed for the robust...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2013
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| Online Access: | https://hdl.handle.net/10356/102760 http://hdl.handle.net/10220/16437 |
| _version_ | 1811689652499775488 |
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| author | Lai, Jian Jiang, Xudong |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Lai, Jian Jiang, Xudong |
| author_sort | Lai, Jian |
| collection | NTU |
| description | This work proposes a novel framework of robust face recognition based on the sparse representation. Image is first divided into modules and each module is processed separately to determine its reliability. A reconstructed image from the modules weighted by their reliability is formed for the robust recognition. We propose to use the modular sparsity and residual jointly to determine the modular reliability. The proposed framework advances both the modular and global sparse representation approaches, especially in dealing with disguise, large illumination variations and expression changes. Compared with the related state-of-the-art methods, experimental results on benchmark face databases verify the advancement of the proposed method. |
| first_indexed | 2024-10-01T05:51:30Z |
| format | Journal Article |
| id | ntu-10356/102760 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:51:30Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/1027602020-03-07T14:00:35Z Modular weighted global sparse representation for robust face recognition Lai, Jian Jiang, Xudong School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering This work proposes a novel framework of robust face recognition based on the sparse representation. Image is first divided into modules and each module is processed separately to determine its reliability. A reconstructed image from the modules weighted by their reliability is formed for the robust recognition. We propose to use the modular sparsity and residual jointly to determine the modular reliability. The proposed framework advances both the modular and global sparse representation approaches, especially in dealing with disguise, large illumination variations and expression changes. Compared with the related state-of-the-art methods, experimental results on benchmark face databases verify the advancement of the proposed method. 2013-10-10T08:33:06Z 2019-12-06T20:59:56Z 2013-10-10T08:33:06Z 2019-12-06T20:59:56Z 2012 2012 Journal Article Lai, J., & Jiang, X. (2012). Modular weighted global sparse representation for robust face recognition. IEEE signal processing letters, 19(9), 571-574. 1070-9908 https://hdl.handle.net/10356/102760 http://hdl.handle.net/10220/16437 10.1109/LSP.2012.2207112 en IEEE signal processing letters © 2012 IEEE |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Lai, Jian Jiang, Xudong Modular weighted global sparse representation for robust face recognition |
| title | Modular weighted global sparse representation for robust face recognition |
| title_full | Modular weighted global sparse representation for robust face recognition |
| title_fullStr | Modular weighted global sparse representation for robust face recognition |
| title_full_unstemmed | Modular weighted global sparse representation for robust face recognition |
| title_short | Modular weighted global sparse representation for robust face recognition |
| title_sort | modular weighted global sparse representation for robust face recognition |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/102760 http://hdl.handle.net/10220/16437 |
| work_keys_str_mv | AT laijian modularweightedglobalsparserepresentationforrobustfacerecognition AT jiangxudong modularweightedglobalsparserepresentationforrobustfacerecognition |