Kinship verification is an emerging task in computer vision with multiple potential applications. However, there's no large enough kinship dataset to train a representative and robust model, which is a limitation for achieving better performance. Moreover, face verification is known to exhibit bias, which has not been dealt with by previous kinship verification works and sometimes even results in serious issues. So we first combine existing kinship datasets and label each identity with the correct race in order to take race information into consideration and provide a larger and complete dataset, called KinRace dataset. Secondly, we propose a multi-task learning model structure with attention module to enhance accuracy, which surpasses state-of-the-art performance. Lastly, our fairness-aware contrastive loss function with adversarial learning greatly mitigates racial bias. We introduce a debias term into traditional contrastive loss and implement gradient reverse in race classification task, which is an innovative idea to mix two fairness methods to alleviate bias. Exhaustive experimental evaluation demonstrates the effectiveness and superior performance of the proposed KFC in both standard deviation and accuracy at the same time.

翻译：亲属关系验证是计算机视觉中一项具有多种潜在应用的新兴任务。然而，目前缺乏足够大的亲属关系数据集来训练具有代表性和鲁棒性的模型，这限制了性能的提升。此外，人脸验证已知存在偏差，而以往的亲属关系验证工作未处理此问题，有时甚至引发严重后果。为此，我们首先整合现有亲属关系数据集，为每个身份标注正确种族以纳入种族信息，构建了一个更大且完整的数据集，称为KinRace数据集。其次，我们提出了一种带有注意力模块的多任务学习模型结构，以提升准确率，其性能超越了现有最优方法。最后，我们提出的结合对抗学习的公平感知对比损失函数显著减轻了种族偏差。我们在传统对比损失中引入去偏项，并在种族分类任务中实现梯度反转，这是一种融合两种公平性方法以缓解偏差的创新思路。详尽的实验评估表明，所提出的KFC方法在标准差和准确率两方面均具有有效性和优越性能。