Regularized locality preserving discriminant embedding for face recognition
For face recognition, graph embedding techniques attempt to produce a high data locality projection for better recognition performance. However, estimation of population data locality could be severely biased due to small number of training samples. The biased estimation triggers overfitting problem...
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| Format: | Article |
| Language: | English |
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Elsevier Science
2012
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| Online Access: | http://shdl.mmu.edu.my/3387/ http://shdl.mmu.edu.my/3387/1/Regularized%20Locality%20Preserving%20Discriminant%20Embedding%20for%20Face%20Recognition.pdf |
| _version_ | 1848790314000580608 |
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| author | Pang, Ying Han Teoh, Andrew Beng Jin Abas, Fazly Salleh |
| author_facet | Pang, Ying Han Teoh, Andrew Beng Jin Abas, Fazly Salleh |
| author_sort | Pang, Ying Han |
| building | MMU Institutional Repository |
| collection | Online Access |
| description | For face recognition, graph embedding techniques attempt to produce a high data locality projection for better recognition performance. However, estimation of population data locality could be severely biased due to small number of training samples. The biased estimation triggers overfitting problem and hence poor generalization. In this paper, we propose a new linear graph embedding technique based upon an adaptive locality preserving regulation model (ALPRM), known as Regularized Locality Preserving Discriminant Embedding (RLPDE). In RLPDE, the projection features are regulated based on ALPRM to approach population data locality, which can directly enhance the locality preserving capability of the projection features. This paper also presents the relation between locality preserving capability and class discrimination. Specifically, we show that the optimization of the locality preserving function minimizes the within-class variability. Experiments on three face datasets such as PIE, FRGC and FERET show the promising performance of the proposed technique. (C) 2011 Elsevier B.V. All rights reserved. |
| first_indexed | 2025-11-14T18:10:38Z |
| format | Article |
| id | mmu-3387 |
| institution | Multimedia University |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T18:10:38Z |
| publishDate | 2012 |
| publisher | Elsevier Science |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | mmu-33872017-01-05T04:15:57Z http://shdl.mmu.edu.my/3387/ Regularized locality preserving discriminant embedding for face recognition Pang, Ying Han Teoh, Andrew Beng Jin Abas, Fazly Salleh QA75.5-76.95 Electronic computers. Computer science For face recognition, graph embedding techniques attempt to produce a high data locality projection for better recognition performance. However, estimation of population data locality could be severely biased due to small number of training samples. The biased estimation triggers overfitting problem and hence poor generalization. In this paper, we propose a new linear graph embedding technique based upon an adaptive locality preserving regulation model (ALPRM), known as Regularized Locality Preserving Discriminant Embedding (RLPDE). In RLPDE, the projection features are regulated based on ALPRM to approach population data locality, which can directly enhance the locality preserving capability of the projection features. This paper also presents the relation between locality preserving capability and class discrimination. Specifically, we show that the optimization of the locality preserving function minimizes the within-class variability. Experiments on three face datasets such as PIE, FRGC and FERET show the promising performance of the proposed technique. (C) 2011 Elsevier B.V. All rights reserved. Elsevier Science 2012-02 Article PeerReviewed text en http://shdl.mmu.edu.my/3387/1/Regularized%20Locality%20Preserving%20Discriminant%20Embedding%20for%20Face%20Recognition.pdf Pang, Ying Han and Teoh, Andrew Beng Jin and Abas, Fazly Salleh (2012) Regularized locality preserving discriminant embedding for face recognition. Neurocomputing, 77 (1). pp. 156-166. ISSN 0925-2312 http://dx.doi.org/10.1016/j.neucom.2011.09.007 doi:10.1016/j.neucom.2011.09.007 doi:10.1016/j.neucom.2011.09.007 |
| spellingShingle | QA75.5-76.95 Electronic computers. Computer science Pang, Ying Han Teoh, Andrew Beng Jin Abas, Fazly Salleh Regularized locality preserving discriminant embedding for face recognition |
| title | Regularized locality preserving discriminant embedding for face recognition |
| title_full | Regularized locality preserving discriminant embedding for face recognition |
| title_fullStr | Regularized locality preserving discriminant embedding for face recognition |
| title_full_unstemmed | Regularized locality preserving discriminant embedding for face recognition |
| title_short | Regularized locality preserving discriminant embedding for face recognition |
| title_sort | regularized locality preserving discriminant embedding for face recognition |
| topic | QA75.5-76.95 Electronic computers. Computer science |
| url | http://shdl.mmu.edu.my/3387/ http://shdl.mmu.edu.my/3387/ http://shdl.mmu.edu.my/3387/ http://shdl.mmu.edu.my/3387/1/Regularized%20Locality%20Preserving%20Discriminant%20Embedding%20for%20Face%20Recognition.pdf |