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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月19日

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  • Paweł Borsukiewicz,
  • Fadi Boutros,
  • Iyiola E. Olatunji,
  • Charles Beumier,
  • Wendkûuni C. Ouédraogo,
  • Jacques Klein,
  • Tegawendé F. Bissyandé

摘要

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.