A modularly vectorized two dimensional LDA for face recognition
In this paper, a modularly vectorized 2DLDA (Mv2DLDA) is proposed for face recognition. First, the original images are divided into modular blocks. Then, each sub-block is transformed into a vector. By using column vector to represent each modular block, we can obtain a two dimensional matrix repres...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
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Institute of Electrical and Electronics Engineers (IEEE)
2012
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| Online Access: | http://hdl.handle.net/20.500.11937/13914 |
| _version_ | 1848748476367634432 |
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| author | Jvmahong, H. Liu, Wan-Quan Lu, C. |
| author2 | Wang, X. |
| author_facet | Wang, X. Jvmahong, H. Liu, Wan-Quan Lu, C. |
| author_sort | Jvmahong, H. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this paper, a modularly vectorized 2DLDA (Mv2DLDA) is proposed for face recognition. First, the original images are divided into modular blocks. Then, each sub-block is transformed into a vector. By using column vector to represent each modular block, we can obtain a two dimensional matrix representation for image. Finally 2DLDA is applied directly on these 2D matrices. Experimental results on ORL, Yale B and PIE databases show that the proposed method can achieve better recognition performance in comparison with RLDA, 2DPCA and 2DLDA. |
| first_indexed | 2025-11-14T07:05:39Z |
| format | Conference Paper |
| id | curtin-20.500.11937-13914 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:05:39Z |
| publishDate | 2012 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-139142023-02-02T07:57:38Z A modularly vectorized two dimensional LDA for face recognition Jvmahong, H. Liu, Wan-Quan Lu, C. Wang, X. Yeung, D. S. In this paper, a modularly vectorized 2DLDA (Mv2DLDA) is proposed for face recognition. First, the original images are divided into modular blocks. Then, each sub-block is transformed into a vector. By using column vector to represent each modular block, we can obtain a two dimensional matrix representation for image. Finally 2DLDA is applied directly on these 2D matrices. Experimental results on ORL, Yale B and PIE databases show that the proposed method can achieve better recognition performance in comparison with RLDA, 2DPCA and 2DLDA. 2012 Conference Paper http://hdl.handle.net/20.500.11937/13914 10.1109/ICMLC.2012.6359009 Institute of Electrical and Electronics Engineers (IEEE) restricted |
| spellingShingle | Jvmahong, H. Liu, Wan-Quan Lu, C. A modularly vectorized two dimensional LDA for face recognition |
| title | A modularly vectorized two dimensional LDA for face recognition |
| title_full | A modularly vectorized two dimensional LDA for face recognition |
| title_fullStr | A modularly vectorized two dimensional LDA for face recognition |
| title_full_unstemmed | A modularly vectorized two dimensional LDA for face recognition |
| title_short | A modularly vectorized two dimensional LDA for face recognition |
| title_sort | modularly vectorized two dimensional lda for face recognition |
| url | http://hdl.handle.net/20.500.11937/13914 |