论文标题
挑战报告：认识到野生数据挑战中的家庭
Challenge report: Recognizing Families In the Wild Data Challenge
论文作者
论文摘要
本文是我们与FG 2020论坛结合使用的野生数据挑战（第4版）中向公认家庭提交的简短报告。自动亲属识别引起了许多研究人员的全面应用，但它仍然是一项非常具有挑战性的任务，因为有限的信息可用于确定一对面孔是否是血液亲戚。在本文中，我们研究了以前的方法并提出了我们的方法。我们尝试许多方法，例如基于深度度学习的方法，为每个图像提取深层嵌入功能，然后根据基于类别的欧几里得距离或方法确定它们是血液亲戚。最后，我们发现一些技巧，例如取样更多的负面样本和高分辨率，可以帮助获得更好的性能。此外，我们提出了一个具有基于二进制分类方法的对称网络，以在所有任务中获得最佳分数。
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.
