We present the largest database for visual kinship recognition, Families In the Wild (FIW), with over 13,000 family photos of 1,000 family trees with 4-to-38 members. It took only a small team to build FIW with efficient labeling tools and work-flow. To extend FIW, we further improved upon this process with a novel semi-automatic labeling scheme that used annotated faces and unlabeled text metadata to discover labels, which were then used, along with existing FIW data, for the proposed clustering algorithm that generated label proposals for all newly added data-both processes are shared and compared in depth, showing great savings in time and human input required. Essentially, the clustering algorithm proposed is semi-supervised and uses labeled data to produce more accurate clusters. We statistically compare FIW to related datasets, which unarguably shows enormous gains in overall size and amount of information encapsulated in the labels. We benchmark two tasks, kinship verification and family classification, at scales incomparably larger than ever before. Pre-trained CNN models fine-tuned on FIW outscores other conventional methods and achieved state-of-the art on the renowned KinWild datasets. We also measure human performance on kinship recognition and compare to a fine-tuned CNN.

中文翻译：

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野外家庭的视觉亲属关系识别


我们提供最大的视觉亲属关系识别数据库，Families In the Wild (FIW)，其中包含 1,000 个家谱（4 至 38 名成员）的 13,000 多张家庭照片。只需一个小团队就可以利用高效的标签工具和工作流程构建 FIW。为了扩展 FIW，我们通过一种新颖的半自动标记方案进一步改进了这个过程，该方案使用带注释的面孔和未标记的文本元数据来发现标签，然后将其与现有的 FIW 数据一起用于生成标签建议的聚类算法对于所有新添加的数据，两个流程都进行共享和深入比较，显示出所需时间和人力投入的巨大节省。本质上，所提出的聚类算法是半监督的，并使用标记数据来产生更准确的聚类。我们在统计上将 FIW 与相关数据集进行比较，毫无疑问，这显示出总体规模和标签中封装的信息量的巨大进步。我们对亲属关系验证和家庭分类这两项任务进行了基准测试，其规模比以往任何时候都大得多。在 FIW 上进行微调的预训练 CNN 模型超越了其他传统方法，并在著名的 KinWild 数据集上达到了最先进的水平。我们还测量人类在亲属关系识别方面的表现，并与微调的 CNN 进行比较。