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A Method for Robust Multispectral Face Recognition

  • Conference paper
Image Analysis and Recognition (ICIAR 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6754))

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Abstract

Matching Short Wave InfraRed (SWIR) face images against a face gallery of color images is a very challenging task. The photometric properties of images in these two spectral bands are highly distinct. This work presents a new cross-spectral face recognition method that encodes both magnitude and phase of responses of a classic bank of Gabor filters applied to multi-spectral face images. Three local operators: Simplified Weber Local Descriptor, Local Binary Pattern, and Generalized Local Binary Pattern are involved. The comparison of encoded face images is performed using the symmetric Kullbuck-Leibler divergence. We show that the proposed method provides high recognition rates at different spectra (visible, Near InfraRed and SWIR). In terms of recognition rates it outperforms Faceit®G8, a commercial software distributed by L1.

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Author information

Authors and Affiliations

  1. Department of CSEE, West Virginia University, Morgantown, WV, USA, 26506-6109

    Francesco Nicolo & Natalia A. Schmid

Authors
  1. Francesco Nicolo
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  2. Natalia A. Schmid
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Waterloo, N2L 3G1, Waterloo, ON, Canada

    Mohamed Kamel

  2. Faculty of Engineering, Institute of Biomedical Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    Aurélio Campilho

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© 2011 Springer-Verlag Berlin Heidelberg

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Nicolo, F., Schmid, N.A. (2011). A Method for Robust Multispectral Face Recognition. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2011. Lecture Notes in Computer Science, vol 6754. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21596-4_19

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  • DOI: https://doi.org/10.1007/978-3-642-21596-4_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21595-7

  • Online ISBN: 978-3-642-21596-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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