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Feature Learning for Facial Kinship Verification

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Facial Kinship Verification

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

Abstract

In this chapter, we discuss feature learning techniques for facial kinship verification. We first review two well-known hand-crafted facial descriptors including local binary patterns (LBP) and the Gabor feature. Then, we introduce a compact binary face descriptor (CBFD) method which learns face descriptors directly from raw pixels. Unlike LBP which samples small-size neighboring pixels and computes binary codes with a fixed coding strategy, CBFD samples large-size neighboring pixels and learn a feature filter to obtain binary codes automatically. Subsequently, we present a prototype-based discriminative feature learning(PDFL) method to learn mid-level discriminative features with low-level descriptor for kinship verification. Unlike most existing prototype-based feature learning methods which learn the model with a strongly labeled training set, this approach works on a large unsupervised generic set combined with a small labeled training set. To better use multiple low-level features for mid-level feature learning, a multiview PDFL (MPDFL) method is further proposed to learn multiple mid-level features to improve the verification performance.

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Notes

  1. 1.

    http://www.kinfacew.com.

  2. 2.

    http://www.kinfacew.com.

  3. 3.

    http://chenlab.ece.cornell.edu/projects/KinshipVerification.

  4. 4.

    http://www.ece.neu.edu/~yunfu/research/Kinface/Kinface.htm.

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Authors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Haibin Yan

  2. Tsinghua University, Beijing, China

    Jiwen Lu

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  1. Haibin Yan
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  2. Jiwen Lu
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Yan, H., Lu, J. (2017). Feature Learning for Facial Kinship Verification. In: Facial Kinship Verification. SpringerBriefs in Computer Science. Springer, Singapore. https://doi.org/10.1007/978-981-10-4484-7_2

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  • DOI: https://doi.org/10.1007/978-981-10-4484-7_2

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  • Print ISBN: 978-981-10-4483-0

  • Online ISBN: 978-981-10-4484-7

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