# Reasoning Graph Networks for Kinship Verification: from Star-shaped to Hierarchical

arXiv: https://arxiv.org/abs/2109.02219 · HTML: https://mlsanity.com/paper/2109.02219/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 70 references that are in the map.
- First posted on arXiv 6 Sep 2021.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Spectral Networks and Locally Connected Networks on Graphs](https://mlsanity.com/paper/1312.6203/) (2013)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [Gated Graph Sequence Neural Networks](https://mlsanity.com/paper/1511.05493/) (2015)
- [Learning Face Representation from Scratch](https://mlsanity.com/paper/1411.7923/) (2014)
- [Deep Convolutional Networks on Graph-Structured Data](https://mlsanity.com/paper/1506.05163/) (2015)
- [Low-resolution Face Recognition in the Wild via Selective Knowledge Distillation](https://mlsanity.com/paper/1811.09998/) (2018)
- [Multi-Modal Graph Neural Network for Joint Reasoning on Vision and Scene Text](https://mlsanity.com/paper/2003.13962/) (2020)
- [BridgeNet: A Continuity-Aware Probabilistic Network for Age Estimation](https://mlsanity.com/paper/1904.03358/) (2019)
- [Deep Graph-Convolutional Image Denoising](https://mlsanity.com/paper/1907.08448/) (2019)
- [Relational Action Forecasting](https://mlsanity.com/paper/1904.04231/) (2019)
- [Trunk-Branch Ensemble Convolutional Neural Networks for Video-based Face Recognition](https://mlsanity.com/paper/1607.05427/) (2016)
- [Graph-Based Social Relation Reasoning](https://mlsanity.com/paper/2007.07453/) (2020)
- [Tri-Subject Kinship Verification: Understanding the Core of A Family](https://mlsanity.com/paper/1501.02555/) (2015)
- [Graph-based Kinship Reasoning Network](https://mlsanity.com/paper/2004.10375/) (2020)
- [Kinship Identification through Joint Learning Using Kinship Verification Ensembles](https://mlsanity.com/paper/2004.06382/) (2020)
- [Supervised Mixed Norm Autoencoder for Kinship Verification in Unconstrained Videos](https://mlsanity.com/paper/1805.12167/) (2018)
- [A Unified Approach to Kinship Verification](https://mlsanity.com/paper/2009.05871/) (2020)
- [Group Membership Prediction](https://mlsanity.com/paper/1509.04783/) (2015)
- [Progressive Multi-Stage Learning for Discriminative Tracking](https://mlsanity.com/paper/2004.00255/) (2020)
- [Learning to Reconstruct and Understand Indoor Scenes from Sparse Views](https://mlsanity.com/paper/1906.07892/) (2019)

## Built on by (most cited first, 3 of 3)

- [3rd Place Scheme on Instance Segmentation Track of ICCV 2021 VIPriors Challenges](https://mlsanity.com/paper/2110.00242/) (2021)
- [A Powerful Face Preprocessing For Robust Kinship Verification based Tensor Analyses](https://mlsanity.com/paper/2312.11290/) (2023)
- [Kinship Verification through a Forest Neural Network](https://mlsanity.com/paper/2504.18910/) (2025)

## Abstract (arXiv)

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.