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

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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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Correspondence to Jian-Gang Wang.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Wang, J., Kong, H., Sung, E. et al. Fusion of Appearance Image and Passive Stereo Depth Map for Face Recognition Based on the Bilateral 2DLDA. J Image Video Proc 2007, 038205 (2007) doi:10.1155/2007/38205

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Keywords

  • Face Recognition
  • Linear Discriminant Analysis
  • Facial Feature
  • Depth Information
  • Active Range