Conference paper
Curvelet-based illumination invariant feature extraction for face recognition
2010 International Conference on Computer Applications and Industrial Electronics, ICCAIE 2010, pp.458-462
International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010), 2010 (Kuala Lumpur, Malaysia, 05-Dec-2010–08-Dec-2010)
Institute of Electrical and Electronics Engineers
2010
Abstract
This paper presents a curvelet-based illumination invariant feature extraction technique to solve the problem of varying illumination in face recognition. Multiband feature technique is employed to search the decomposed curvelet subbands for subbands which are insensitive to illumination variation. The two best performing subbands are then concatenated to form the Optimal Curvelet Subbands (OCS). To further improve the performance of OSC, histogram equalization is applied to enhance the contrast of the details. The proposed feature extraction method was evaluated on YaleB, EYaleB and AR database. The simulation results obtained shows that the proposed method outperforms its wavelet counterpart and that the extracted subbands are also applicable for other databases.
Details
- Title
- Curvelet-based illumination invariant feature extraction for face recognition
- Authors
- Sue Inn Ch'ng (Author) - University of Nottingham Malaysia CampusKah Phooi Seng (Author) - University of Nottingham Malaysia CampusLi-Minn Ang (Author) - University of Nottingham Malaysia Campus
- Publication details
- 2010 International Conference on Computer Applications and Industrial Electronics, ICCAIE 2010, pp.458-462
- Conference details
- International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010), 2010 (Kuala Lumpur, Malaysia, 05-Dec-2010–08-Dec-2010)
- Publisher
- Institute of Electrical and Electronics Engineers
- Date published
- 2010
- DOI
- 10.1109/ICCAIE.2010.5735123
- ISBN
- 9781424490530
- Copyright note
- © 2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
- Organisation Unit
- University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering; Engage Research Lab
- Language
- English
- Record Identifier
- 99513902002621
- Output Type
- Conference paper
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