A Unified Approach to Kinship Verification

Eran Dahan, Yosi Keller

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

In this work, we propose a deep learning-based approach for kin verification using a unified multi-task learning scheme where all kinship classes are jointly learned. This allows us to better utilize small training sets that are typical of kin verification. We introduce a novel approach for fusing the embeddings of kin images, to avoid overfitting, which is a common issue in training such networks. An adaptive sampling scheme is derived for the training set images, to resolve the inherent imbalance in kin verification datasets. A thorough ablation study exemplifies the effectivity of our approach, which is experimentally shown to outperform contemporary state-of-the-art kin verification results when applied to the Families In the Wild, FG2018, and FG2020 datasets.

Original languageEnglish
Article number9257100
Pages (from-to)2851-2857
Number of pages7
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume43
Issue number8
DOIs
StatePublished - 1 Aug 2021

Bibliographical note

Publisher Copyright:
© 1979-2012 IEEE.

Keywords

  • Kinship verification
  • convolutional neural networks
  • face biometrics
  • face recognition
  • multi-task learning

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