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Machine Vision and Applications

, Volume 29, Issue 5, pp 873–890 | Cite as

Kinship verification from facial images and videos: human versus machine

  • Miguel Bordallo Lopez
  • Abdenour Hadid
  • Elhocine Boutellaa
  • Jorge Goncalves
  • Vassilis Kostakos
  • Simo Hosio
Original Paper
  • 168 Downloads

Abstract

Automatic kinship verification from facial images is a relatively new and challenging research problem in computer vision. It consists in automatically determining whether two persons have a biological kin relation by examining their facial attributes. In this work, we compare the performance of humans and machines in kinship verification tasks. We investigate the state-of-the-art methods in automatic kinship verification from facial images, comparing their performance with the one obtained by asking humans to complete an equivalent task using a crowdsourcing system. Our results show that machines can consistently beat humans in kinship classification tasks in both images and videos. In addition, we study the limitations of currently available kinship databases and analyzing their possible impact in kinship verification experiment and this type of comparison.

Keywords

Kinship verification Face analysis Biometrics Crowdsourcing 

Notes

Acknowledgements

The support of the Academy of Finland is fully acknowledged.

Supplementary material

138_2018_943_MOESM1_ESM.pdf (287 kb)
Supplementary material 1 (pdf 287 KB)

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© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Center for Machine Vision and Signal AnalysisUniversity of OuluOuluFinland
  2. 2.School of Computing and Information SystemsUniversity of MelbourneMelbourneAustralia
  3. 3.Center for Ubiquitous ComputingUniversity of OuluOuluFinland

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