Graph-based Kinship Reasoning Network
This addresses kinship verification for applications like forensics or social media, but it is incremental as it builds on existing feature extraction methods.
The paper tackles kinship verification by proposing a graph-based kinship reasoning (GKR) network that focuses on comparing and fusing features from image pairs, outperforming state-of-the-art methods on KinFaceW-I and KinFaceW-II datasets.
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.