Mediterranean Journal of Modeling and Simulation
Volume 4, Numéro 1, Pages 015-036
Authors : Soltane Mohamed .
In this paper, the use of finite Gaussian mixture modal (GMM) tuned using Expectation Maximization (EM) estimating algorithms for score level data fusion is proposed. Automated biometric systems for human identification measure a “signature” of the human body, compare the resulting characteristic to a database, and render an application dependent decision. These biometric systems for personal authentication and identification are based upon physiological or behavioral features which are typically distinctive, Multi-biometric systems, which consolidate information from multiple biometric sources, are gaining popularity because they are able to overcome limitations such as non-universality, noisy sensor data, large intra-user variations and susceptibility to spoof attacks that are commonly encountered in mono modal biometric systems. Simulation result show that finite mixture modal (GMM) is quite effective in modelling the genuine and impostor score densities, fusion based the product of Likelihood Ratio achieves a significant performance on eNTERFACE 2005 multi-biometric database based on dynamic face, on-line signature and text independent speech modalities.
Biometry; Multi-Modal; Authentication; Face Recognition; Speaker and Signature Verification; Data Fusion; Adaptive Bayesian decision; GMM; EM; Likelihood Ratio.