Video-based kinship verification using distance metric learning. (March 2018)

• Record Type: Journal Article
• Authors: Yan, Haibin; Hu, Junlin
• Is Part Of: Pattern recognition. Volume 75(2018:Mar.)
• Page Start: 15
• Page End: 24
• Publication Date: 2018-03
• Subjects: Kinship verification -- Metric learning -- Face recognition -- Video-based; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2017.03.001
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We investigate the problem of kinship verification from facial videos. We present a new video face dataset for the video-based kinship verification study. We develop a benchmark to evaluate state-of-the-art metric learning methods in video-based kinship verification. Experiments show the efficacy of distance metric learning in kinship verification. Abstract: In this paper, we investigate the problem of video-based kinship verification via human face analysis. While several attempts have been made on facial kinship verification from still images, to our knowledge, the problem of video-based kinship verification has not been formally addressed in the literature. In this paper, we make the two contributions to video-based kinship verification. On one hand, we present a new video face dataset called Kinship Face Videos in the Wild (KFVW) which were captured in wild conditions for the video-based kinship verification study, as well as the standard benchmark. On the other hand, we employ our benchmark to evaluate and compare the performance of several state-of-the-art metric learning based kinship verification methods. Experimental results are presented to demonstrate the efficacy of our proposed dataset and the effectiveness of existing metric learning methods for video-based kinship verification. Lastly, we also evaluate human ability on kinship verification from facial videos and experimental results show that metric learning based computational methods are not as good as that of human observers.