Recognising faces in unseen modes: a tensor based approach
This paper addresses the limitation of current multilinear techniques (multilinear PCA, multilinear ICA) when applied to face recognition for handling faces in unseen illumination and viewpoints. We propose a new recognition method, exploiting the interaction of all the subspaces resulting from mult...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE
2008
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| Online Access: | http://hdl.handle.net/20.500.11937/6414 |
| _version_ | 1848745069615513600 |
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| author | Rana, Santu Liu, Wan-Quan Lazarescu, Mihai Venkatesh, Svetha |
| author2 | NA |
| author_facet | NA Rana, Santu Liu, Wan-Quan Lazarescu, Mihai Venkatesh, Svetha |
| author_sort | Rana, Santu |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper addresses the limitation of current multilinear techniques (multilinear PCA, multilinear ICA) when applied to face recognition for handling faces in unseen illumination and viewpoints. We propose a new recognition method, exploiting the interaction of all the subspaces resulting from multilinear decomposition (for both multilinear PCA and ICA), to produce a new basis called multilinear-eigenmodes. This basis offers the flexibility to handle face images at unseen illumination or viewpoints. Experiments on benchmarked datasets yield superior performance in terms of both accuracy and computational cost. |
| first_indexed | 2025-11-14T06:11:30Z |
| format | Conference Paper |
| id | curtin-20.500.11937-6414 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:11:30Z |
| publishDate | 2008 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-64142018-03-29T09:05:44Z Recognising faces in unseen modes: a tensor based approach Rana, Santu Liu, Wan-Quan Lazarescu, Mihai Venkatesh, Svetha NA This paper addresses the limitation of current multilinear techniques (multilinear PCA, multilinear ICA) when applied to face recognition for handling faces in unseen illumination and viewpoints. We propose a new recognition method, exploiting the interaction of all the subspaces resulting from multilinear decomposition (for both multilinear PCA and ICA), to produce a new basis called multilinear-eigenmodes. This basis offers the flexibility to handle face images at unseen illumination or viewpoints. Experiments on benchmarked datasets yield superior performance in terms of both accuracy and computational cost. 2008 Conference Paper http://hdl.handle.net/20.500.11937/6414 10.1109/CVPR.2008.4587813 IEEE restricted |
| spellingShingle | Rana, Santu Liu, Wan-Quan Lazarescu, Mihai Venkatesh, Svetha Recognising faces in unseen modes: a tensor based approach |
| title | Recognising faces in unseen modes: a tensor based approach |
| title_full | Recognising faces in unseen modes: a tensor based approach |
| title_fullStr | Recognising faces in unseen modes: a tensor based approach |
| title_full_unstemmed | Recognising faces in unseen modes: a tensor based approach |
| title_short | Recognising faces in unseen modes: a tensor based approach |
| title_sort | recognising faces in unseen modes: a tensor based approach |
| url | http://hdl.handle.net/20.500.11937/6414 |