While kinship verification is a well-exploited task which only identifies whether or not two people are kins, kinship identification is the task to further identify the particular type of kinships and is not well exploited yet. We found that a naive extension of kinship verification cannot solve the identification properly. This is because the existing verification networks are individually trained on specific kinships and do not consider the context between different kinship types. Also, the existing kinship verification dataset has a biased positive-negative distribution, which is different from real-world distribution. To solve it, we propose a novel kinship identification approach through the joint training of kinship verification ensembles and a Joint Identification Module. We also propose to rebalance the training dataset to make it realistic. Rigorous experiments demonstrate the superiority of performance on kinship identification task. It also demonstrates significant performance improvement of kinship verification when trained on the same unbiased data.

本文提出了一种新的亲属关系识别方法，该方法基于关系验证集合和分类模块的联合训练，并建议将训练数据集重新平衡以更符合真实分布。在大规模实验中，该方法对于亲属关系的识别具有较好的性能表现，并且同一数据集上基于该方法训练的亲属关系验证也实现了显著性能提升。

基于联合学习的亲属关系识别：利用亲属验证集成