Challenge report: Recognizing Families In the Wild Data Challenge

TL;DR

This paper reports on a kinship recognition challenge, exploring deep learning methods and tricks like high resolution and negative sampling to improve accuracy in identifying blood relatives from face images.

Contribution

It introduces a symmetric network with binary classification and evaluates various deep metric learning approaches for kinship recognition.

Findings

Deep embedding features improve kinship recognition accuracy.

High resolution images enhance model performance.

Sampling more negative pairs boosts results.

Abstract

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.