In this paper, we investigate the problem of facial kinship verification by learning hierarchical reasoning graph networks. Conventional methods usually focus on learning discriminative features for each facial image of a paired sample and neglect how to fuse the obtained two facial image features and reason about the relations between them. To address this, we propose a Star-shaped Reasoning Graph Network (S-RGN). Our S-RGN first constructs a star-shaped graph where each surrounding node encodes the information of comparisons in a feature dimension and the central node is employed as the bridge for the interaction of surrounding nodes. Then we perform relational reasoning on this star graph with iterative message passing. The proposed S-RGN uses only one central node to analyze and process information from all surrounding nodes, which limits its reasoning capacity. We further develop a Hierarchical Reasoning Graph Network (H-RGN) to exploit more powerful and flexible capacity. More specifically, our H-RGN introduces a set of latent reasoning nodes and constructs a hierarchical graph with them. Then bottom-up comparative information abstraction and top-down comprehensive signal propagation are iteratively performed on the hierarchical graph to update the node features. Extensive experimental results on four widely used kinship databases show that the proposed methods achieve very competitive results.

中文翻译：

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用于亲缘关系验证的推理图网络：从星形到分层

在本文中，我们通过学习层次推理图网络来研究面部亲属关系验证的问题。常规方法通常集中于学习成对样本的每个面部图像的判别特征，而忽略如何融合所获得的两个面部图像特征以及它们之间的关系的原因。为了解决这个问题，我们提出了一个星型推理图网络（S-RGN）。我们的S-RGN首先构造一个星形图，其中每个周围节点在特征维度上对比较信息进行编码，而中心节点被用作周围节点交互的桥梁。然后，我们通过迭代消息传递对该星形图执行关系推理。拟议的S-RGN仅使用一个中央节点来分析和处理来自所有周围节点的信息，这限制了它的推理能力。我们进一步开发了层次推理图网络（H-RGN），以利用更强大，更灵活的功能。更具体地说，我们的H-RGN引入了一组潜在推理节点，并使用它们构建了层次图。然后，在层次图上迭代执行自下而上的比较信息抽象和自上而下的综合信号传播，以更新节点特征。在四个广泛使用的亲属关系数据库上的大量实验结果表明，所提出的方法取得了非常有竞争力的结果。然后，在层次图上迭代执行自下而上的比较信息抽象和自上而下的综合信号传播，以更新节点特征。在四个广泛使用的亲属关系数据库上的大量实验结果表明，所提出的方法取得了非常有竞争力的结果。然后，在层次图上迭代执行自下而上的比较信息抽象和自上而下的综合信号传播，以更新节点特征。在四个广泛使用的亲属关系数据库上的大量实验结果表明，所提出的方法取得了非常有竞争力的结果。