Open Access

Fusion of Appearance Image and Passive Stereo Depth Map for Face Recognition Based on the Bilateral 2DLDA

  • Jian-Gang Wang1Email author,
  • Hui Kong2,
  • Eric Sung2,
  • Wei-Yun Yau1 and
  • EamKhwang Teoh2
EURASIP Journal on Image and Video Processing20072007:038205

https://doi.org/10.1155/2007/38205

Received: 27 April 2006

Accepted: 18 June 2007

Published: 28 August 2007

Abstract

This paper presents a novel approach for face recognition based on the fusion of the appearance and depth information at the match score level. We apply passive stereoscopy instead of active range scanning as popularly used by others. We show that present-day passive stereoscopy, though less robust and accurate, does make positive contribution to face recognition. By combining the appearance and disparity in a linear fashion, we verified experimentally that the combined results are noticeably better than those for each individual modality. We also propose an original learning method, the bilateral two-dimensional linear discriminant analysis (B2DLDA), to extract facial features of the appearance and disparity images. We compare B2DLDA with some existing 2DLDA methods on both XM2VTS database and our database. The results show that the B2DLDA can achieve better results than others.

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Authors’ Affiliations

(1)
Institute for Infocomm Research
(2)
School of Electrical and Electronic Engineering, Nanyang Technological University

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Copyright

© Jian-GangWang et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.