Abstract In this paper, we propose a deep relational network which exploits multi-scale information of facial images for kinship verification. Unlike most existing deep learning based facial kinship verification methods which employ convolutional neural networks to extract holistic features, we present a deep model to exploit facial kinship relationship from local regions. For each given pair of face images, our method uses two convolutional neural networks which share parameters to extract different scales of features, which are expected to provide global contextual information of face images. We split a set of features at the same scale into multiple groups, where different groups capture information of different local regions. For each pair of local feature groups which are extracted from the same scale and position, we propose a relation network to reason their relationship, and use a verification network to infer the kin relation based on the results of local relations from different facial regions. We conduct experiments on two widely used facial kinship datasets: KinFaceW-I and KinFaceW-II, and our experimental results are presented to demonstrate the effectiveness of our approach.

中文翻译：

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面部亲属关系验证的多尺度深度关系推理

摘要 在本文中，我们提出了一种深度关系网络，该网络利用面部图像的多尺度信息进行亲属关系验证。与大多数现有的基于深度学习的面部亲属关系验证方法采用卷积神经网络来提取整体特征不同，我们提出了一个深度模型来利用局部区域的面部亲属关系。对于每对给定的人脸图像，我们的方法使用两个共享参数的卷积神经网络来提取不同尺度的特征，这些特征有望提供人脸图像的全局上下文信息。我们将一组相同尺度的特征分成多个组，其中不同的组捕获不同局部区域的信息。对于从相同尺度和位置提取的每对局部特征组，我们提出了一个关系网络来推理他们的关系，并使用验证网络根据来自不同面部区域的局部关系的结果来推断亲属关系。我们在两个广泛使用的面部亲属数据集上进行了实验：KinFaceW-I 和 KinFaceW-II，并展示了我们的实验结果以证明我们方法的有效性。