| Título: | State of the Art: Signature Biometrics Verification |
| Autores: |
Mohamed Soltane; “Badji Mokhtar” University of Annaba Noureddine Doghmane; “Badji Mokhtar” University of Annaba Nourddine Guersi; “Badji Mokhtar” University of Annaba |
| Fecha: | 2010-04-10 |
| Publicador: | BRAIN: Broad Research in Artificial Intelligence and Neuroscience |
| Fuente: |
 |
| Tipo: | Works in progress, Discussions |
| Tema: |
Biometrics Biometric authentication, behavioral, signature, soft decision and Gaussian Mixture Modal, EM, GEM and FJ |
| Descripción: | 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. |
| Idioma: | Inglés |
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