论文标题
基于图的亲属推理网络
Graph-based Kinship Reasoning Network
论文作者
论文摘要
在本文中，我们提出了一个基于图的亲属关系推理（GKR）网络，用于亲属验证，该网络旨在有效地对图像对提取的特征进行关系推理。与主要关注如何学习判别特征的大多数现有方法不同，我们的方法考虑如何比较和融合提取的特征对来推理亲属关系。提出的GKR构造了一个称为亲属关系图的星形图，其中每个外围节点代表一个特征维度中的信息比较，并且中央节点用作外围节点之间信息通信的桥梁。然后，GKR在此图上执行关系推理，并通过递归消息传递。 Kinfacew-I和KinfaceW-II数据集的广泛实验结果表明，所提出的GKR优于最新方法。
In this paper, we propose a graph-based kinship reasoning (GKR) network for kinship verification, which aims to effectively perform relational reasoning on the extracted features of an image pair. Unlike most existing methods which mainly focus on how to learn discriminative features, our method considers how to compare and fuse the extracted feature pair to reason about the kin relations. The proposed GKR constructs a star graph called kinship relational graph where each peripheral node represents the information comparison in one feature dimension and the central node is used as a bridge for information communication among peripheral nodes. Then the GKR performs relational reasoning on this graph with recursive message passing. Extensive experimental results on the KinFaceW-I and KinFaceW-II datasets show that the proposed GKR outperforms the state-of-the-art methods.
