文章基本信息

标题：State of the Art: Signature Biometrics Verification
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作者：Mohamed Soltane ; Noureddine Doghmane ; Nourddine Guersi 等
期刊名称：Brain. Broad Research in Artificial Intelligence and Neuroscience
印刷版ISSN：2067-3957
出版年度：2010
卷号：1
期号：2
页码：133-142
语种：English
出版社：EduSoft publishing
摘要：This paper presents a comparative analysis of the performance of three estimation algorithms: Expectation Maximization (EM), Greedy EM Algorithm (GEM) and Figueiredo-Jain Algorithm (FJ) - based on the Gaussian mixture models (GMMs) for signature biometrics verification. The simulation results have shown significant performance achievements. The test performance of EER=5.49 % for "EM", EER=5.04 % for "GEM" and EER=5.00 % for "FJ", shows that the behavioral information scheme of signature biometrics is robust and has a discriminating power, which can be explored for identity authentication.
关键词：Biometric authentication, behavioral, signature, soft decision and Gaussian Mixture Modal, EM, GEM and FJ