University of Oulu

Bordallo Lopez, M., Hadid, A., Boutellaa, E. et al. Machine Vision and Applications (2018) 29: 873.

Kinship verification from facial images and videos : human versus machine

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Author: Bordallo Lopez, Miguel1; Hadid, Abdenour1; Boutellaa, Elhocine1;
Organizations: 1Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland
2School of Computing and Information Systems, University of Melbourne, Melbourne, Australia
3Center for Ubiquitous Computing, University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.8 MB)
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Language: English
Published: Springer Nature, 2018
Publish Date: 2019-09-05


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.

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Series: Machine vision and applications
ISSN: 0932-8092
ISSN-E: 1432-1769
ISSN-L: 0932-8092
Volume: 29
Issue: 5
Pages: 873 - 890
DOI: 10.1007/s00138-018-0943-x
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
Funding: The support of the Academy of Finland is fully acknowledged.
Copyright information: © Springer-Verlag GmbH Germany, part of Springer Nature 2018. This is a post-peer-review, pre-copyedit version of an article published in Machine Vision and Applications. The final authenticated version is available online at: