# A Multi-Task Comparator Framework for Kinship Verification

arXiv: https://arxiv.org/abs/2006.01615 · HTML: https://mlsanity.com/paper/2006.01615/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2020.

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

- [The 5th Recognizing Families in the Wild Data Challenge: Predicting Kinship from Faces](https://mlsanity.com/paper/2111.00598/) (2021)
- [Kinship Representation Learning with Face Componential Relation](https://mlsanity.com/paper/2304.04546/) (2023)
- [Supervised Contrastive Learning and Feature Fusion for Improved Kinship Verification](https://mlsanity.com/paper/2302.09556/) (2023)

## Abstract (arXiv)

Approaches for kinship verification often rely on cosine distances between face identification features. However, due to gender bias inherent in these features, it is hard to reliably predict whether two opposite-gender pairs are related. Instead of fine tuning the feature extractor network on kinship verification, we propose a comparator network to cope with this bias. After concatenating both features, cascaded local expert networks extract the information most relevant for their corresponding kinship relation. We demonstrate that our framework is robust against this gender bias and achieves comparable results on two tracks of the RFIW Challenge 2020. Moreover, we show how our framework can be further extended to handle partially known or unknown kinship relations.