State of the Art: Signature Biometrics Verification

Abstract

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.

Keywords

• Biometric authentication, behavioral, signature, soft decision and Gaussian Mixture Modal, EM, GEM and FJ