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MWIR-to-Visible and LWIR-to-Visible Face Recognition Using Partial Least Squares and Dictionary Learning

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Face Recognition Across the Imaging Spectrum

Abstract

Cross-spectral face recognition, which seeks to match a face image acquired in one spectral band (e.g., infrared) to that of a face acquired in another band (e.g., visible), is a relatively new area of research in the biometrics community. Thermal-to-visible face recognition has been receiving increasing attention, due to its promising potential for low-light or nighttime surveillance and intelligence gathering applications . However, matching a thermal probe image to a visible face database is highly challenging. Thermal imaging is emission dominated, acquiring thermal radiation naturally emitted by facial tissue, while visible imaging is reflection dominated, acquiring light reflected from the surface of the face. The resulting difference between the thermal face signature and the visible face signature renders conventional algorithms designed for within-spectral matching (e.g., visible-to-visible) unsuitable for thermal-to-visible face recognition. In this chapter, two thermal-to-visible face recognition approaches are discussed: (1) a partial least squares (PLS) -based approach and (2) a dictionary learning  SVM approach. Preprocessing and feature extraction techniques used to correlate the signatures in the feature subspace are also discussed. We present recognition results on an extensive multimodal face dataset containing facial imagery acquired under different experimental conditions. Furthermore, we discuss key findings and implications for MWIR-to-visible and LWIR-to-visible face recognition. Finally, a novel imaging technique for acquiring an unprecedented level of facial detail in thermal images, polarimetric LWIR, is presented along with a framework for performing cross-spectral face recognition .

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Authors and Affiliations

  1. US Army Research Laboratory, Adelphi, MD, USA

    Shuowen Hu, Nathaniel J. Short, Prudhvi K. Gurram, Kristan P. Gurton & Christopher Reale

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  1. Shuowen Hu
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  2. Nathaniel J. Short
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  3. Prudhvi K. Gurram
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  4. Kristan P. Gurton
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  5. Christopher Reale
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Correspondence to Shuowen Hu .

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Editors and Affiliations

  1. Multispectral Imagery Lab—MILab, LCSEE, West Virginia University, MORGANTOWN, West Virginia, USA

    Thirimachos Bourlai

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Hu, S., Short, N.J., Gurram, P.K., Gurton, K.P., Reale, C. (2016). MWIR-to-Visible and LWIR-to-Visible Face Recognition Using Partial Least Squares and Dictionary Learning. In: Bourlai, T. (eds) Face Recognition Across the Imaging Spectrum. Springer, Cham. https://doi.org/10.1007/978-3-319-28501-6_4

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  • DOI: https://doi.org/10.1007/978-3-319-28501-6_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28499-6

  • Online ISBN: 978-3-319-28501-6

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