A statistical model for signature verification

• 成果类型: Article
• 署名作者: McKeague, IW
• 署名单位: Columbia University; State University System of Florida; Florida State University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214504000000827
• 发表日期: 2005
• 页码: 231-241
• 关键词: Nonparametric regression; asymptotic equivalence; white-noise; spline; identification; REPRESENTATION; RECOGNITION; inference; tracking

摘要

A Bayesian model for off-line signature verification involving the representation of a signature through its curvature is developed. The prior model makes use of a spatial point process for specifying the knots in an approximation restricted to a buffer region close to a template curvature, along with an independent time-warping mechanism. In this way, prior shape information about the signature can be built into the analysis. The observation model is based on additive white noise superimposed on the underlying curvature. The approach is implemented using Markov chain Monte Carlo and applied to a collection of documented instances of William Shakespeare's signature.