Robust blurred palmprint recognition via the fast Vese-Osher model
In this paper, we propose a new palmprint recognition system by using the fast Vese-Osher decomposition model to process the blurred palmprint images. First, a Gaussian defocus degradation model (GDDM) is proposed to extract the structure layer and texture layer of blurred palmprint images by using...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Published: |
Springer Verlag
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/41742 |
| _version_ | 1848756228872732672 |
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| author | Hong, D. Liu, Wan-Quan Su, J. Pan, Z. Wu, X. |
| author_facet | Hong, D. Liu, Wan-Quan Su, J. Pan, Z. Wu, X. |
| author_sort | Hong, D. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this paper, we propose a new palmprint recognition system by using the fast Vese-Osher decomposition model to process the blurred palmprint images. First, a Gaussian defocus degradation model (GDDM) is proposed to extract the structure layer and texture layer of blurred palmprint images by using the fast Vese-Osher decomposition model, and the structure layer is proved to be more stable and robust than texture layer for palmprint recognition. Second, a novel algorithm based on weighted robustness with histogram of oriented gradient (WRHOG) is proposed to extract robust features from the structure layer of blurred palmprint images, which can address the problem of translation and rotation to a large extent. Finally, the normalized correlation coefficient (NCC) is used to measure the similarity of palmprint features for the new recognition system. Extensive experiments on the PolyU palmprint database and the blurred PolyU palmprint database validate the effectiveness of the proposed recognition system. |
| first_indexed | 2025-11-14T09:08:52Z |
| format | Journal Article |
| id | curtin-20.500.11937-41742 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:08:52Z |
| publishDate | 2014 |
| publisher | Springer Verlag |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-417422017-09-13T14:17:43Z Robust blurred palmprint recognition via the fast Vese-Osher model Hong, D. Liu, Wan-Quan Su, J. Pan, Z. Wu, X. In this paper, we propose a new palmprint recognition system by using the fast Vese-Osher decomposition model to process the blurred palmprint images. First, a Gaussian defocus degradation model (GDDM) is proposed to extract the structure layer and texture layer of blurred palmprint images by using the fast Vese-Osher decomposition model, and the structure layer is proved to be more stable and robust than texture layer for palmprint recognition. Second, a novel algorithm based on weighted robustness with histogram of oriented gradient (WRHOG) is proposed to extract robust features from the structure layer of blurred palmprint images, which can address the problem of translation and rotation to a large extent. Finally, the normalized correlation coefficient (NCC) is used to measure the similarity of palmprint features for the new recognition system. Extensive experiments on the PolyU palmprint database and the blurred PolyU palmprint database validate the effectiveness of the proposed recognition system. 2014 Journal Article http://hdl.handle.net/20.500.11937/41742 10.1007/978-3-662-45261-5_24 Springer Verlag restricted |
| spellingShingle | Hong, D. Liu, Wan-Quan Su, J. Pan, Z. Wu, X. Robust blurred palmprint recognition via the fast Vese-Osher model |
| title | Robust blurred palmprint recognition via the fast Vese-Osher model |
| title_full | Robust blurred palmprint recognition via the fast Vese-Osher model |
| title_fullStr | Robust blurred palmprint recognition via the fast Vese-Osher model |
| title_full_unstemmed | Robust blurred palmprint recognition via the fast Vese-Osher model |
| title_short | Robust blurred palmprint recognition via the fast Vese-Osher model |
| title_sort | robust blurred palmprint recognition via the fast vese-osher model |
| url | http://hdl.handle.net/20.500.11937/41742 |