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Feature Learning for Facial Kinship Verification

  • Haibin Yan
  • Jiwen Lu
Chapter
Part of the SpringerBriefs in Computer Science book series (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.

Keywords

Face Image Local Binary Pattern Binary Code Restricted Boltzmann Machine Local Binary Pattern Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© The Author(s) 2017

Authors and Affiliations

  1. 1.Beijing University of Posts and TelecommunicationsBeijingChina
  2. 2.Tsinghua UniversityBeijingChina

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