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Kinship Verification Based on Deep Learning

  • Chapter
Deep Learning in Object Detection and Recognition

Abstract

Automatic kinship verification using facial images is a relatively new and challenging research problem in computer vision. It consists in automatically predicting whether two persons have a biological kin relation by examining their facial attributes. While most of the existing works extract shallow handcrafted features from still face images, in this chapter, we approach this problem from deep learning point of view. Promising results, especially those of deep features, are obtained on the benchmark UvA-NEMO Smile database and KinFaceW-I and KinFaceW-II kinship face databases.

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Authors and Affiliations

  1. School of Electronics and Information, Northwestern Polytechnical University, Xi’an, China

    Xiaoting Wu, Xiaoyi Feng & Lei Li

  2. Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland

    Elhocine Boutellaa & Abdenour Hadid

Authors
  1. Xiaoting Wu
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  2. Xiaoyi Feng
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  3. Lei Li
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  4. Elhocine Boutellaa
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  5. Abdenour Hadid
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Corresponding author

Correspondence to Xiaoyi Feng .

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Editors and Affiliations

  1. School of Electronics and Information, Northwestern Polytechnical University, Xi’an, Shaanxi, China

    Xiaoyue Jiang  & Xiaoyi Feng  & 

  2. Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Oulu, Finland

    Abdenour Hadid

  3. School of Electrical and Information Engineering, Tianjin University, Tianjin, Tianjin, China

    Yanwei Pang

  4. École de technologie supérieure, University of Québec, Montréal, QC, Canada

    Eric Granger

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© 2019 Springer Nature Singapore Pte Ltd.

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Wu, X., Feng, X., Li, L., Boutellaa, E., Hadid, A. (2019). Kinship Verification Based on Deep Learning. In: Jiang, X., Hadid, A., Pang, Y., Granger, E., Feng, X. (eds) Deep Learning in Object Detection and Recognition. Springer, Singapore. https://doi.org/10.1007/978-981-10-5152-4_5

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  • DOI: https://doi.org/10.1007/978-981-10-5152-4_5

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-5151-7

  • Online ISBN: 978-981-10-5152-4

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