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CrossEncoder: Towards 3D-Free Depth Face Recovery and Fusion Scheme for Heterogeneous Face Recognition

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Representations, Analysis and Recognition of Shape and Motion from Imaging Data (RFMI 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 842))

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Abstract

As a worthwhile trade-off between fully 2D and fully 3D based face recognition (FR), 2D/3D asymmetric face recognition has recently emerged which involves matching two face representations from these two alternative modalities. Different from most previous work which rely on accurately registered 3D face templates, we address this issue by proposing a novel multi-task deep convolutional neural network (CNN) architecture based on 2.5D images. With an autoencoder-like pipeline and a specially formulated criterion, our approach is capable of parallelizing real-time 2.5D face image reconstruction and discriminative face feature extraction. Further, through both qualitative and quantitative experiments on commonly used FRGC 2D/3D face database, we demonstrate that our framework could achieve satisfactory performance on both tasks while being drastically efficient.

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Authors and Affiliations

  1. LIRIS Laboratory, Ecole Centrale de Lyon, Ecully, France

    Wuming Zhang & Liming Chen

Authors
  1. Wuming Zhang
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  2. Liming Chen
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Corresponding author

Correspondence to Wuming Zhang .

Editor information

Editors and Affiliations

  1. Ecole Centrale de Lyon/LIRIS, Lyon, France

    Liming Chen

  2. Cité Scientifique, IMT Lille Douai/CRIStAL, Villeneuve-d'Ascq Cedex, France

    Boulbaba Ben Amor

  3. Ecole Nationale des Sciences de l’Informatique de Tunisie/CRISTAL, Manouba, Tunisia

    Faouzi Ghorbel

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Zhang, W., Chen, L. (2019). CrossEncoder: Towards 3D-Free Depth Face Recovery and Fusion Scheme for Heterogeneous Face Recognition. In: Chen, L., Ben Amor, B., Ghorbel, F. (eds) Representations, Analysis and Recognition of Shape and Motion from Imaging Data. RFMI 2017. Communications in Computer and Information Science, vol 842. Springer, Cham. https://doi.org/10.1007/978-3-030-19816-9_8

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  • DOI: https://doi.org/10.1007/978-3-030-19816-9_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-19815-2

  • Online ISBN: 978-3-030-19816-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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