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A Scalable Patch-Based Approach for RGB-D Face Recognition

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Neural Information Processing (ICONIP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9950))

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Abstract

This paper presents a novel approach for face recognition using low cost RGB-D cameras under challenging conditions. In particular, the proposed approach is based on salient points to extract local patches independently to the face pose. The classification is performed using a scalable sparse representation classification by an adaptive and dynamic dictionaries selection. The experimental results proved that the proposed algorithm achieves significant accuracy on three different RGB-D databases and competes with known approaches in the literature.

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Author information

Authors and Affiliations

  1. Multimedia InfoRmation Systems and Advanced Computing Laboratory (MIRACL), Sfax University, Road Sokra Km 3 BP 802, 3018, Sfax, Tunisia

    Nesrine Grati & Mohamed Hammami

  2. Driving Assistance Research Center, Valeo Vision, 34 rue St-André Z.I. des Vignes, 93012, Bobigny, France

    Achraf Ben-Hamadou

Authors
  1. Nesrine Grati
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  2. Achraf Ben-Hamadou
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  3. Mohamed Hammami
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Corresponding author

Correspondence to Nesrine Grati .

Editor information

Editors and Affiliations

  1. The University of Tokyo , Tokyo, Japan

    Akira Hirose

  2. Kobe University , Kobe, Japan

    Seiichi Ozawa

  3. Okinawa Institute of Science and Technology Graduate University, Onna, Japan

    Kenji Doya

  4. Nara Institute of Science and Technology , Ikoma, Japan

    Kazushi Ikeda

  5. Kyungpook National University , Daegu, Korea (Republic of)

    Minho Lee

  6. Chinese Academy of Sciences , Beijing, China

    Derong Liu

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Grati, N., Ben-Hamadou, A., Hammami, M. (2016). A Scalable Patch-Based Approach for RGB-D Face Recognition. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9950. Springer, Cham. https://doi.org/10.1007/978-3-319-46681-1_35

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  • DOI: https://doi.org/10.1007/978-3-319-46681-1_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46680-4

  • Online ISBN: 978-3-319-46681-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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