Video-based kinship verification using distance metric learning

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.