# Graph-based Kinship Reasoning Network

arXiv: https://arxiv.org/abs/2004.10375 · HTML: https://mlsanity.com/paper/2004.10375/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2020.

## Built on by (most cited first, 8 of 8)

- [Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression](https://mlsanity.com/paper/2103.13629/) (2021)
- [Structure-Aware Face Clustering on a Large-Scale Graph with bf107 Nodes](https://mlsanity.com/paper/2103.13225/) (2021)
- [Meta-Mining Discriminative Samples for Kinship Verification](https://mlsanity.com/paper/2103.15108/) (2021)
- [Reasoning Graph Networks for Kinship Verification: from Star-shaped to Hierarchical](https://mlsanity.com/paper/2109.02219/) (2021)
- [Kinship Representation Learning with Face Componential Relation](https://mlsanity.com/paper/2304.04546/) (2023)
- [Kinship Verification Based on Cross-Generation Feature Interaction Learning](https://mlsanity.com/paper/2109.02809/) (2021)
- [Confidence-Calibrated Face and Kinship Verification](https://mlsanity.com/paper/2210.13905/) (2022)
- [Deep Collaborative Multi-Modal Learning for Unsupervised Kinship Estimation](https://mlsanity.com/paper/2109.02804/) (2021)

## Abstract (arXiv)

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.