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Improved nuisance attribute projection for face recognition

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Abstract

Th e illumination variation is one of the well-known problems in face recognition under uncontrolled environments. Several techniques have been presented in the literature to cope up with this problem. Lately, a technique known as Nuisance Attribute Projection (NAP), originally developed for the speaker recognition field was introduced to image processing in order to compensate for luminance artifacts. This paper extends and improves the earlier work by exploring efficient methodologies for using NAP for face recognition under varied illumination conditions. In particular, we propose a modified NAP formulation and show that NAP training can be simplified for face recognition. Additionally, we suggested a compact framework merging between NAP compensation and eigenface recognition. A series of experiments using the extended YaleB database, and a cross-validation using the PIE CMU and the Oulo databases are performed to validate our proposals.

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Acknowledgments

The authors would like to thank Vitomir Struc for his helpful comments and to Ralph Gross for his assistance with the PIE database.

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Authors and Affiliations

  1. Department of Electro-Optics Engineering, Ben-Gurion University, Beer Sheva, Israel

    Ariel Yifrach, Eitan Novoselsky & Yitzhak Yitzhaky

  2. Technology Section, Israel National Police, Jerusalem, Israel

    Ariel Yifrach & Yosef A. Solewicz

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  1. Ariel Yifrach
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  2. Eitan Novoselsky
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  3. Yosef A. Solewicz
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  4. Yitzhak Yitzhaky
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Correspondence to Ariel Yifrach.

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Yifrach, A., Novoselsky, E., Solewicz, Y.A. et al. Improved nuisance attribute projection for face recognition. Pattern Anal Applic 19, 69–78 (2016). https://doi.org/10.1007/s10044-014-0388-4

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