Beyond Real Faces: Synthetic Datasets Can Achieve Reliable Recognition Performance without Privacy Compromise

2025-10-20

AI Summary

Facial recognition faces privacy infringement and regulatory compliance risks—e.g., under GDPR—arising from real-world data collection. This work systematically evaluates the feasibility of synthetic facial data and, for the first time, proposes and empirically validates seven core privacy-preserving synthetic-data criteria, including identity leakage prevention, intra-class diversity, and inter-class separability. Leveraging multi-million-sample experiments, we conduct comprehensive evaluation across multiple benchmarks (e.g., CASIA-WebFace), assessing recognition accuracy, identity separation, intra-class variation, and fairness. Results show that the top-performing synthetic datasets—VariFace and VIGFace—achieve 95.67% and 94.91% accuracy, respectively, surpassing the real-world CASIA-WebFace benchmark (94.70%). Moreover, they enable controllable bias mitigation and ethically aligned generation. Our study establishes high-fidelity synthetic facial data as a scientifically sound, technically viable, and ethically necessary alternative paradigm.

Technology Category

• Computer Vision: Bias, Fairness & Privacy
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

The deployment of facial recognition systems has created an ethical dilemma: achieving high accuracy requires massive datasets of real faces collected without consent, leading to dataset retractions and potential legal liabilities under regulations like GDPR. While synthetic facial data presents a promising privacy-preserving alternative, the field lacks comprehensive empirical evidence of its viability. This study addresses this critical gap through extensive evaluation of synthetic facial recognition datasets. We present a systematic literature review identifying 25 synthetic facial recognition datasets (2018-2025), combined with rigorous experimental validation. Our methodology examines seven key requirements for privacy-preserving synthetic data: identity leakage prevention, intra-class variability, identity separability, dataset scale, ethical data sourcing, bias mitigation, and benchmark reliability. Through experiments involving over 10 million synthetic samples, extended by a comparison of results reported on five standard benchmarks, we provide the first comprehensive empirical assessment of synthetic data's capability to replace real datasets. Best-performing synthetic datasets (VariFace, VIGFace) achieve recognition accuracies of 95.67% and 94.91% respectively, surpassing established real datasets including CASIA-WebFace (94.70%). While those images remain private, publicly available alternatives Vec2Face (93.52%) and CemiFace (93.22%) come close behind. Our findings reveal that they ensure proper intra-class variability while maintaining identity separability. Demographic bias analysis shows that, even though synthetic data inherits limited biases, it offers unprecedented control for bias mitigation through generation parameters. These results establish synthetic facial data as a scientifically viable and ethically imperative alternative for facial recognition research.

Problem

• Evaluating synthetic facial data as privacy-preserving alternative to real datasets
• Assessing identity leakage prevention and bias mitigation in synthetic faces
• Validating synthetic data performance against standard facial recognition benchmarks

Innovation

• Uses synthetic facial datasets for privacy protection
• Achieves high recognition accuracy without real faces
• Enables bias mitigation through controlled generation parameters