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Kinship Identification Through Joint Learning Using Kinship Verification Ensembles

机译：通过使用亲属验证集合通过联合学习识别

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摘要

Kinship verification is a well-explored task: identifying whether or not two persons are kin. In contrast, kinship identification has been largely ignored so far. Kinship identification aims to further identify the particular type of kinship. An extension to kinship verification run short to properly obtain identification, because existing verification networks are individually trained on specific kinships and do not consider the context between different kinship types. Also, existing kinship verification datasets have biased positive-negative distributions which are different than real-world distributions. To this end, we propose a novel kinship identification approach based on joint training of kinship verification ensembles and classification modules. We propose to rebalance the training dataset to become more realistic. Large scale experiments demonstrate the appealing performance on kinship identification. The experiments further show significant performance improvement of kinship verification when trained on the same dataset with more realistic distributions.
机译：亲属验证是一个探索的任务：识别两个人是否是亲属。相比之下，到目前为止，血缘关系识别已经很大程度上被忽略了。亲属识别旨在进一步识别特定类型的亲属关系。 KILSHIP验证的扩展运行短暂以正确获取身份证明，因为现有的验证网络在特定的亲属关系上单独培训，并且不考虑不同亲属类型之间的上下文。此外，现有的亲属验证数据集具有与现实世界分布不同的正负分布。为此，我们提出了一种基于亲属验证集合和分类模块的联合培训的新的亲属识别方法。我们建议重新平衡培训数据集变得更加现实。大规模实验表明了亲属识别的吸引力。实验进一步显示了在具有更现实的分布的同一数据集上培训时的亲属性验证的显着性能提高。

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