Face recognition based on curvelets and local binary pattern features via using local property preservation
In this paper, we propose a new feature extraction approach for face recognition based on Curvelet transform and local binary pattern operator. The motivation of this approach is based on two observations. One is that Curvelet transform is a new anisotropic multi-resolution analysis tool, which can...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
Elsevier Inc.
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/18105 |
| _version_ | 1848749649003806720 |
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| author | Zhou, L. Liu, Wan-Quan Lu, Z. Nie, T. |
| author_facet | Zhou, L. Liu, Wan-Quan Lu, Z. Nie, T. |
| author_sort | Zhou, L. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this paper, we propose a new feature extraction approach for face recognition based on Curvelet transform and local binary pattern operator. The motivation of this approach is based on two observations. One is that Curvelet transform is a new anisotropic multi-resolution analysis tool, which can effectively represent image edge discontinuities; the other is that local binary pattern operator is one of the best current texture descriptors for face images. As the curvelet features in different frequency bands represent different information of the original image, we extract such features using different methods for different frequency bands. Technically, the lowest frequency band component is processed using the local binary pattern method, and only the medium frequency band components are normalized. And then, we combine them to create a feature set, and use the local preservation projection to reduce its dimension. Finally, we classify the test samples using the nearest neighbor classifier in the reduced space. Extensive experiments on the Yale database, the extended Yale B database, the PIE pose 09 database, and the FRGC database illustrate the effectiveness of the proposed method. © 2014 Elsevier B.V. All rights reserved. |
| first_indexed | 2025-11-14T07:24:17Z |
| format | Journal Article |
| id | curtin-20.500.11937-18105 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:24:17Z |
| publishDate | 2014 |
| publisher | Elsevier Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-181052017-09-13T13:43:08Z Face recognition based on curvelets and local binary pattern features via using local property preservation Zhou, L. Liu, Wan-Quan Lu, Z. Nie, T. In this paper, we propose a new feature extraction approach for face recognition based on Curvelet transform and local binary pattern operator. The motivation of this approach is based on two observations. One is that Curvelet transform is a new anisotropic multi-resolution analysis tool, which can effectively represent image edge discontinuities; the other is that local binary pattern operator is one of the best current texture descriptors for face images. As the curvelet features in different frequency bands represent different information of the original image, we extract such features using different methods for different frequency bands. Technically, the lowest frequency band component is processed using the local binary pattern method, and only the medium frequency band components are normalized. And then, we combine them to create a feature set, and use the local preservation projection to reduce its dimension. Finally, we classify the test samples using the nearest neighbor classifier in the reduced space. Extensive experiments on the Yale database, the extended Yale B database, the PIE pose 09 database, and the FRGC database illustrate the effectiveness of the proposed method. © 2014 Elsevier B.V. All rights reserved. 2014 Journal Article http://hdl.handle.net/20.500.11937/18105 10.1016/j.jss.2014.04.037 Elsevier Inc. restricted |
| spellingShingle | Zhou, L. Liu, Wan-Quan Lu, Z. Nie, T. Face recognition based on curvelets and local binary pattern features via using local property preservation |
| title | Face recognition based on curvelets and local binary pattern features via using local property preservation |
| title_full | Face recognition based on curvelets and local binary pattern features via using local property preservation |
| title_fullStr | Face recognition based on curvelets and local binary pattern features via using local property preservation |
| title_full_unstemmed | Face recognition based on curvelets and local binary pattern features via using local property preservation |
| title_short | Face recognition based on curvelets and local binary pattern features via using local property preservation |
| title_sort | face recognition based on curvelets and local binary pattern features via using local property preservation |
| url | http://hdl.handle.net/20.500.11937/18105 |