论文标题
用于亲属验证的多任务比较框架
A Multi-Task Comparator Framework for Kinship Verification
论文作者
论文摘要
亲属验证方法通常依赖于面部识别特征之间的余弦距离。但是，由于这些特征固有的性别偏差，很难可靠地预测两个相反的性别对是否相关。我们没有在亲属验证上对特征提取器网络进行微调，而是提出一个比较网络来应对这种偏见。连接这两个功能后，级联的本地专家网络提取了与其相应亲属关系最相关的信息。我们证明，我们的框架与这种性别偏见是有力的，并在RFIW挑战2020的两条轨道上取得了可比的结果。此外，我们展示了如何进一步扩展我们的框架以处理部分已知或未知的亲属关系。
Approaches for kinship verification often rely on cosine distances between face identification features. However, due to gender bias inherent in these features, it is hard to reliably predict whether two opposite-gender pairs are related. Instead of fine tuning the feature extractor network on kinship verification, we propose a comparator network to cope with this bias. After concatenating both features, cascaded local expert networks extract the information most relevant for their corresponding kinship relation. We demonstrate that our framework is robust against this gender bias and achieves comparable results on two tracks of the RFIW Challenge 2020. Moreover, we show how our framework can be further extended to handle partially known or unknown kinship relations.
