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Machine Vision and Applications
Erschienen in: Machine Vision and Applications 5/2018

29.05.2018 | Original Paper

Kinship verification from facial images and videos: human versus machine

verfasst von: Miguel Bordallo Lopez, Abdenour Hadid, Elhocine Boutellaa, Jorge Goncalves, Vassilis Kostakos, Simo Hosio

Erschienen in: Machine Vision and Applications | Ausgabe 5/2018

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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.
Vorheriger Artikel Scene text recognition using residual convolutional recurrent neural network
Nächster Artikel Temporal segmentation and recognition of team activities in sports

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Metadaten
Titel
Kinship verification from facial images and videos: human versus machine
verfasst von
Miguel Bordallo Lopez
Abdenour Hadid
Elhocine Boutellaa
Jorge Goncalves
Vassilis Kostakos
Simo Hosio
Publikationsdatum
29.05.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
Machine Vision and Applications / Ausgabe 5/2018
Print ISSN: 0932-8092
Elektronische ISSN: 1432-1769
DOI
https://doi.org/10.1007/s00138-018-0943-x

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