Unsupervised Face Recognition using Unlabeled Synthetic Data

TL;DR

This paper introduces USynthFace, an unsupervised face recognition model trained on unlabeled synthetic data, utilizing extensive augmentations to achieve high accuracy while addressing privacy concerns.

Contribution

It presents a novel unsupervised face recognition approach using synthetic data and diverse augmentations, bypassing the need for labeled authentic datasets.

Findings

Achieves high recognition accuracy with synthetic data

Effective augmentation strategies improve model performance

Demonstrates privacy-friendly face recognition solution

Abstract

Over the past years, the main research innovations in face recognition focused on training deep neural networks on large-scale identity-labeled datasets using variations of multi-class classification losses. However, many of these datasets are retreated by their creators due to increased privacy and ethical concerns. Very recently, privacy-friendly synthetic data has been proposed as an alternative to privacy-sensitive authentic data to comply with privacy regulations and to ensure the continuity of face recognition research. In this paper, we propose an unsupervised face recognition model based on unlabeled synthetic data (USynthFace). Our proposed USynthFace learns to maximize the similarity between two augmented images of the same synthetic instance. We enable this by a large set of geometric and color transformations in addition to GAN-based augmentation that contributes to the USynthFace model training. We also conduct numerous empirical studies on different components of our USynthFace. With the proposed set of augmentation operations, we proved the effectiveness of our USynthFace in achieving relatively high recognition accuracies using unlabeled synthetic data.