Face recognition based on manifold constrained joint sparse sensing with K-SVD
© 2018 Springer Science+Business Media, LLC, part of Springer Nature Face recognition based on Sparse representation idea has recently become an important research topic in computer vision community. However, the dictionary learning process in most of the existing approaches suffers from the perturb...
| Main Authors: | , , , , , , , , |
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| Format: | Journal Article |
| Published: |
Springer
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/69140 |
| _version_ | 1848761977974816768 |
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| author | Liu, J. Liu, Wan-Quan Ma, S. Lu, Chong Xiu, X. Pathirage, N. Li, Ling Chen, G. Zeng, W. |
| author_facet | Liu, J. Liu, Wan-Quan Ma, S. Lu, Chong Xiu, X. Pathirage, N. Li, Ling Chen, G. Zeng, W. |
| author_sort | Liu, J. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2018 Springer Science+Business Media, LLC, part of Springer Nature Face recognition based on Sparse representation idea has recently become an important research topic in computer vision community. However, the dictionary learning process in most of the existing approaches suffers from the perturbations brought by the variations of the input samples, since the consistence of the learned dictionaries from similar input samples based on K-SVD are not well addressed in the existing literature. In this paper, we will propose a novel technique for dictionary learning based on K-SVD to address the consistence issue. In particular, the proposed method embeds the manifold constraints into a standard dictionary learning framework based on k-SVD and force the optimization process to satisfy the structure preservation requirement. Therefore, this new approach can consistently integrate the manifold constraints during the optimization process, and it can contribute a better solution which is robust to the variance of the input samples. Extensive experiments on several popular face databases show a consistent performance improvement in comparison to some related state-of-the-art algorithms. |
| first_indexed | 2025-11-14T10:40:15Z |
| format | Journal Article |
| id | curtin-20.500.11937-69140 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:40:15Z |
| publishDate | 2018 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-691402018-06-29T12:35:25Z Face recognition based on manifold constrained joint sparse sensing with K-SVD Liu, J. Liu, Wan-Quan Ma, S. Lu, Chong Xiu, X. Pathirage, N. Li, Ling Chen, G. Zeng, W. © 2018 Springer Science+Business Media, LLC, part of Springer Nature Face recognition based on Sparse representation idea has recently become an important research topic in computer vision community. However, the dictionary learning process in most of the existing approaches suffers from the perturbations brought by the variations of the input samples, since the consistence of the learned dictionaries from similar input samples based on K-SVD are not well addressed in the existing literature. In this paper, we will propose a novel technique for dictionary learning based on K-SVD to address the consistence issue. In particular, the proposed method embeds the manifold constraints into a standard dictionary learning framework based on k-SVD and force the optimization process to satisfy the structure preservation requirement. Therefore, this new approach can consistently integrate the manifold constraints during the optimization process, and it can contribute a better solution which is robust to the variance of the input samples. Extensive experiments on several popular face databases show a consistent performance improvement in comparison to some related state-of-the-art algorithms. 2018 Journal Article http://hdl.handle.net/20.500.11937/69140 10.1007/s11042-018-6071-9 Springer restricted |
| spellingShingle | Liu, J. Liu, Wan-Quan Ma, S. Lu, Chong Xiu, X. Pathirage, N. Li, Ling Chen, G. Zeng, W. Face recognition based on manifold constrained joint sparse sensing with K-SVD |
| title | Face recognition based on manifold constrained joint sparse sensing with K-SVD |
| title_full | Face recognition based on manifold constrained joint sparse sensing with K-SVD |
| title_fullStr | Face recognition based on manifold constrained joint sparse sensing with K-SVD |
| title_full_unstemmed | Face recognition based on manifold constrained joint sparse sensing with K-SVD |
| title_short | Face recognition based on manifold constrained joint sparse sensing with K-SVD |
| title_sort | face recognition based on manifold constrained joint sparse sensing with k-svd |
| url | http://hdl.handle.net/20.500.11937/69140 |