Challenge report: Recognizing Families In the Wild Data Challenge
This is an incremental improvement for researchers in computer vision, addressing the challenging task of kinship recognition from limited facial data.
The paper tackled automatic kinship recognition by proposing a symmetric network with binary classification, achieving the best score in all tasks of the Recognizing Families In the Wild Data Challenge.
This paper is a brief report to our submission to the Recognizing Families In the Wild Data Challenge (4th Edition), in conjunction with FG 2020 Forum. Automatic kinship recognition has attracted many researchers' attention for its full application, but it is still a very challenging task because of the limited information that can be used to determine whether a pair of faces are blood relatives or not. In this paper, we studied previous methods and proposed our method. We try many methods, like deep metric learning-based, to extract deep embedding feature for every image, then determine if they are blood relatives by Euclidean distance or method based on classes. Finally, we find some tricks like sampling more negative samples and high resolution that can help get better performance. Moreover, we proposed a symmetric network with a binary classification based method to get our best score in all tasks.