Augmenting Automated Kinship Verification with Targeted Human Input

Authors

Danula Hettiachchi, The University of MelbourneFollow
Niels van Berkel, Aalborg UniversityFollow
Simo Hosio, University of OuluFollow
Miguel Bordallo López, VTT Research Centre of FinlandFollow
Vassilis Kostakos, The University of MelbourneFollow
Jorge Goncalves, The University of MelbourneFollow

Abstract

Kinship verification is the problem whereby a third party determines whether two people are related. Despite previous research in Psychology and Machine Vision, the factors affecting a person’s verification ability are poorly understood. Through an online crowdsourcing study, we investigate the impact of gender, race and medium type (image vs video) on kinship verification - taking into account the demographics of both raters and ratees. A total of 325 workers completed over 50,000 kinship verification tasks consisting of pairs of faces shown in images and videos from three widely used datasets. Our results identify an own-race bias and a higher verification accuracy for same-gender image pairs than opposite-gender image pairs. Our results demonstrate that humans can still outperform current state-of-the-art automated unsupervised approaches. Furthermore, we show that humans perform better when presented with videos instead of still images. Our findings contribute to the design of future human-in-the-loop kinship verification tasks, including time-critical use cases such as identifying missing persons.

Recommended Citation

Hettiachchi, Danula; van Berkel, Niels; Hosio, Simo; Bordallo López, Miguel; Kostakos, Vassilis; and Goncalves, Jorge, "Augmenting Automated Kinship Verification with Targeted Human Input" (2020). PACIS 2020 Proceedings. 141.
https://aisel.aisnet.org/pacis2020/141