# Challenge report: Recognizing Families In the Wild Data Challenge

arXiv: https://arxiv.org/abs/2006.00154 · HTML: https://mlsanity.com/paper/2006.00154/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 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)

This paper is a brief report to our submission to the Recognizing Families In the Wild Data Challenge (4th Edition), in conjunction with FG 2020 Forum. Automatic kinship recognition has attracted many researchers' attention for its full application, but it is still a very challenging task because of the limited information that can be used to determine whether a pair of faces are blood relatives or not. In this paper, we studied previous methods and proposed our method. We try many methods, like deep metric learning-based, to extract deep embedding feature for every image, then determine if they are blood relatives by Euclidean distance or method based on classes. Finally, we find some tricks like sampling more negative samples and high resolution that can help get better performance. Moreover, we proposed a symmetric network with a binary classification based method to get our best score in all tasks.