SIG: A Synthetic Identity Generation Pipeline for Generating Evaluation Datasets for Face Recognition

Synthetic data can democratize AI evaluation: The paper addresses a critical barrier in responsible AI—the difficulty and expense of creating ethically-sourced evaluation datasets. By using synthetic faces rather than scraped internet data, the SIG pipeline enables smaller organizations and researchers to fairly evaluate face recognition systems without the resources required for large-scale, consented data collection.

Controllable synthetic datasets enable fairness testing: ControlFace10k, the released dataset of 10,008 synthetic face images, is deliberately balanced across race, gender, and age demographics. This controlled composition allows researchers to systematically test whether face recognition systems perform equally well across different demographic groups—a key measure of fairness that's difficult to achieve with real-world scraped data.

Synthetic evaluation data addresses privacy and consent concerns: The paper highlights that evaluation datasets are often scraped from the internet without user consent, creating ethical violations and regulatory compliance issues. Synthetic data eliminates this problem entirely, allowing organizations to build evaluation datasets that respect privacy regulations while maintaining the ability to assess model performance.

Quality and representativeness matter for evaluation: The paper emphasizes that face recognition system performance and fairness "depend significantly on the quality and representativeness of evaluation datasets." By generating synthetic identities with controllable pose, facial features, and demographic attributes, SIG enables more rigorous and comprehensive testing than datasets limited by whatever data happens to be available online.