Journal article
Block-based Deep Belief Networks for face recognition
International Journal of Biometrics, Vol.4(2), pp.130-143
2012
Abstract
This paper presents research findings on the use of Deep Belief Networks (DBNs) for face recognition. Experiments were conducted to compare the performance of a DBN trained using whole images with that of several DBN trained using image blocks. Image blocks are obtained when the face images are divided into smaller blocks. The objective of using image blocks is to improve the performance of the present DBN to visual variations. To test this hypothesis, the proposed block-based DBN was tested on different databases, which contain a variety of visual variations. Simulation results on these databases show that the proposed block-based DBN is effective against lighting variation. The proposed approach is also compared with other illumination invariant methods and was found to demonstrate higher recognition accuracies.
Details
- Title
- Block-based Deep Belief Networks for face recognition
- Authors
- Sue Inn Ch'ng (Author) - University of Nottingham Malaysia CampusK P Seng (Author) - Sunway UniversityLi-Minn Ang (Author) - Edith Cowan University
- Publication details
- International Journal of Biometrics, Vol.4(2), pp.130-143
- Publisher
- Inderscience Publishers
- Date published
- 2012
- DOI
- 10.1504/IJBM.2012.046247
- ISSN
- 1755-8301; 1755-831X; 1755-8301
- Organisation Unit
- University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering; Engage Research Lab
- Language
- English
- Record Identifier
- 99513794702621
- Output Type
- Journal article
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