# Kinship Representation Learning with Face Componential Relation

arXiv: https://arxiv.org/abs/2304.04546 · HTML: https://mlsanity.com/paper/2304.04546/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 46 references that are in the map.
- First posted on arXiv 10 Apr 2023.

## Builds on (most cited first, 15 of 15)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [AdaFace: Quality Adaptive Margin for Face Recognition](https://mlsanity.com/paper/2204.00964/) (2022)
- [SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness](https://mlsanity.com/paper/2003.09373/) (2020)
- [Regular Polytope Networks](https://mlsanity.com/paper/2103.15632/) (2021)
- [Trunk-Branch Ensemble Convolutional Neural Networks for Video-based Face Recognition](https://mlsanity.com/paper/1607.05427/) (2016)
- [Graph-based Kinship Reasoning Network](https://mlsanity.com/paper/2004.10375/) (2020)
- [Deep Fusion Siamese Network for Automatic Kinship Verification](https://mlsanity.com/paper/2006.00143/) (2020)
- [Achieving Better Kinship Recognition Through Better Baseline](https://mlsanity.com/paper/2006.11739/) (2020)
- [Maximally Compact and Separated Features with Regular Polytope Networks](https://mlsanity.com/paper/2301.06116/) (2023)
- [Meta-Mining Discriminative Samples for Kinship Verification](https://mlsanity.com/paper/2103.15108/) (2021)
- [Challenge report: Recognizing Families In the Wild Data Challenge](https://mlsanity.com/paper/2006.00154/) (2020)
- [A Multi-Task Comparator Framework for Kinship Verification](https://mlsanity.com/paper/2006.01615/) (2020)
- [A Unified Approach to Kinship Verification](https://mlsanity.com/paper/2009.05871/) (2020)

## Built on by (most cited first, 2 of 2)

- [KFC: Kinship Verification with Fair Contrastive Loss and Multi-Task Learning](https://mlsanity.com/paper/2309.10641/) (2023)
- [Kinship Verification through a Forest Neural Network](https://mlsanity.com/paper/2504.18910/) (2025)

## Abstract (arXiv)

Kinship recognition aims to determine whether the subjects in two facial images are kin or non-kin, which is an emerging and challenging problem. However, most previous methods focus on heuristic designs without considering the spatial correlation between face images. In this paper, we aim to learn discriminative kinship representations embedded with the relation information between face components (e.g., eyes, nose, etc.). To achieve this goal, we propose the Face Componential Relation Network, which learns the relationship between face components among images with a cross-attention mechanism, which automatically learns the important facial regions for kinship recognition. Moreover, we propose Face Componential Relation Network (FaCoRNet), which adapts the loss function by the guidance from cross-attention to learn more discriminative feature representations. The proposed FaCoRNet outperforms previous state-of-the-art methods by large margins for the largest public kinship recognition FIW benchmark.