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arXiv Machine Learning By Lulu Xie, Yancheng Wang, Kanchan Chowdhury, Rolando Garcia, Yingzhen Yang, Jia Zou

IDSPACE: A Novel Document Generator for Reliable Evaluation of Digital Identity Verification Systems [Extended Technical Report]

Read the original on arXiv Machine Learning →

IDSPACE is a new synthetic document generator designed to aid the evaluation of digital identity verification systems. It uses model‑guided Bayesian optimization to tune generation parameters for visual similarity and prediction consistency, separates user‑defined metadata from low‑level control settings, and supports scanned and mobile‑captured documents. Experiments demonstrate that IDSPACE improves evaluation consistency by 15‑45 % over baselines, boosts training accuracy by up to 9 %, and increases SSIM similarity by 10 %, while providing a dataset of 359,240 synthetic documents across ten European ID types.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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