Title: Block-based Deep Belief Networks for face recognition

Authors: Sue Inn Ch'ng; Kah Phooi Seng; Li-Minn Ang

Addresses: School of Engineering, Nottingham University Malaysia Campus, Jalan Broga, 43500 Semyih, Selangor, Malaysia. ' School of Computer Technology, Sunway University Malaysia, 5 Jalan Universiti, Bandar Sunway, 46150 Petaling Jaya, Selangor. ' Centre for Communications Engineering Research, Edith Cowan University, Australia

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.

Keywords: face recognition; DBNs; deep belief networks; image blocks; fusion scheme; expression variation; illumination variation; decision-level fusion; score-level fusion; biometrics.

DOI: 10.1504/IJBM.2012.046247

International Journal of Biometrics, 2012 Vol.4 No.2, pp.130 - 143

Received: 09 Apr 2011
Accepted: 03 Aug 2011

Published online: 25 Mar 2012 *

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