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Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions

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Analysis and Modeling of Faces and Gestures (AMFG 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4778))

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Abstract

Recognition in uncontrolled situations is one of the most important bottlenecks for practical face recognition systems. We address this by combining the strengths of robust illumination normalization, local texture based face representations and distance transform based matching metrics. Specifically, we make three main contributions: (i) we present a simple and efficient preprocessing chain that eliminates most of the effects of changing illumination while still preserving the essential appearance details that are needed for recognition; (ii) we introduce Local Ternary Patterns (LTP), a generalization of the Local Binary Pattern (LBP) local texture descriptor that is more discriminant and less sensitive to noise in uniform regions; and (iii) we show that replacing local histogramming with a local distance transform based similarity metric further improves the performance of LBP/LTP based face recognition. The resulting method gives state-of-the-art performance on three popular datasets chosen to test recognition under difficult illumination conditions: Face Recognition Grand Challenge version 1 experiment 4, Extended Yale-B, and CMU PIE.

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Authors and Affiliations

  1. INRIA & Laboratoire Jean Kuntzmann, 655 avenue de l’Europe, Montbonnot 38330, France

    Xiaoyang Tan & Bill Triggs

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  1. Xiaoyang Tan
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  2. Bill Triggs
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S. Kevin Zhou Wenyi Zhao Xiaoou Tang Shaogang Gong

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Tan, X., Triggs, B. (2007). Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions. In: Zhou, S.K., Zhao, W., Tang, X., Gong, S. (eds) Analysis and Modeling of Faces and Gestures. AMFG 2007. Lecture Notes in Computer Science, vol 4778. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75690-3_13

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  • DOI: https://doi.org/10.1007/978-3-540-75690-3_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-75689-7

  • Online ISBN: 978-3-540-75690-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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