Video-based Parent-Child Relationship Prediction

In this paper, we investigate the problem of video-based parent-child relationship prediction via human face analysis. Most existing kinship verification methods predict the parent-child relationship from single images, which cannot effectively utilize videos of human faces for kinship verification. Recently, there have been a few methods for parent-child relationship prediction based on face videos, but all of them only perform pairwise comparisons between human faces between a single parent and a single child. Thus, they cannot effectively combine information about both the father's and the mother's faces when judging the kin relationship. In this paper, we propose a new dtaaset, Familyship Face Videos in the Wild (FFVW), which was captured both in wild conditions and standard reference, to deal with this issue. The inputs of FFVW are three separate videos of a family. To our best knowledge, our paper is the first attempt at addressing this problem. In our pre-processing step, we extract four key frames from each video, before doing facial recognition and alignment. Finally, we use a convolutional neural network to make the prediction. Overall, the effectiveness of this approach is verified by experimental results, which show that our dataset outperforms previous approaches to parent-child relationship prediction.