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 humanin-the-loop kinship verification tasks, including time-critical use cases such as
identifying missing persons.
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 humanin-the-loop kinship verification tasks, including time-critical use cases such as
identifying missing persons.
Originalsprog | Engelsk |
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Titel | Proceedings of the Pacific Asia Conference on Information Systems |
Antal sider | 14 |
Forlag | Association for Information Systems |
Publikationsdato | 2020 |
Sider | 141:1-141:14 |
Status | Udgivet - 2020 |
Begivenhed | Pacific Asia Conference on Information Systems - Dubai, Saudi-Arabien Varighed: 20 jun. 2020 → 24 jun. 2020 |
Konference
Konference | Pacific Asia Conference on Information Systems |
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Land/Område | Saudi-Arabien |
By | Dubai |
Periode | 20/06/2020 → 24/06/2020 |