Options
2023
Conference Paper
Title
Unsupervised Face Recognition using Unlabeled Synthetic Data
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. The training code and pretrained model are publicly available under https://github.com/fdbtrs/Unsupervised-Face-Recognition-using-Unlabeled-Synthetic-Data.
Author(s)
Funder
Hessisches Ministerium für Wissenschaft und Kunst
Keyword(s)
Branche: Information Technology
Research Line: Computer vision (CV)
Research Line: Machine learning (ML)
LTA: Interactive decision-making support and assistance systems
LTA: Machine intelligence, algorithms, and data structures (incl. semantics)
Biometrics
Face recognition
Privacy enhancing technologies
Computer vision
Deep learning
ATHENE
CRISP