Augmenting Automated Kinship Verification with Targeted Human Input (bibtex)
by Hettiachchi, Danula, van Berkel, Niels, Hosio, Simo, Lopez, Miguel Bordallo, Kostakos, Vassilis and Goncalves, Jorge
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.
Reference:
D. Hettiachchi, N. van Berkel, S. Hosio, M. B. Lopez, V. Kostakos, J. Goncalves, "Augmenting Automated Kinship Verification with Targeted Human Input", in Proceedings of the Pacific Asia Conference on Information Systems (PACIS'20), 2020, 141:1-141:14.
Bibtex Entry:
@inproceedings{Hettiachchi2020KinshipHuman,
	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.},
	Author = {Hettiachchi, Danula and van Berkel, Niels and Hosio, Simo and Lopez, Miguel Bordallo and Kostakos, Vassilis and Goncalves, Jorge},
	Booktitle = {Proceedings of the Pacific Asia Conference on Information Systems},
	Location = {PACIS'20},
	Pages = {141:1-141:14},
	Title = {Augmenting Automated Kinship Verification with Targeted Human Input},
	Url = {https://nielsvanberkel.com/files/publications/pacis2020a.pdf},
	Year = {2020}}
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