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

EURASIP Journal on Image and Video Processingvolume 2007, Article number: 038205 (2007) | Download Citation

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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Affiliations

  1. Institute for Infocomm Research, 21 Heng Mui Keng Terrace, 119613, Singapore

    • Jian-Gang Wang
    •  & Wei-Yun Yau

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore

    • Hui Kong
    • , Eric Sung
    •  & EamKhwang Teoh

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Correspondence to Jian-Gang Wang.

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Keywords

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