Beyond real faces: synthetic datasets can achieve reliable recognition performance without privacy compromise

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 retractions of datasets and potential legal liabilities. 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 an extensive evaluation of synthetic facial recognition datasets. We present a systematic literature review identifying 25 synthetic facial recognition datasets, combined with rigorous experimental validation. Our methodology examines seven key requirements for privacy-preserving synthetic data. Through experiments, extended by a comparison of results reported on five standard benchmarks, we provide the first comprehensive empirical assessment that establishes synthetic facial data as a scientifically viable and ethically imperative alternative for facial recognition research.