文章基本信息

标题：Soft Decision Level Fusion Approach to a Combined Behavioral Speech-Signature Biometrics Verification
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作者：Mohamed Soltane ; Mimen Bakhti
期刊名称：International Journal of Signal Processing, Image Processing and Pattern Recognition
印刷版ISSN：2005-4254
出版年度：2013
卷号：6
期号：1
出版社：SERSC
摘要：This paper describes a combined behavioral techniques based on speech and signature biometrics modalities. Fusion of multiple biometric modalities for human verification performance improvement has received considerable attention. 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. Soft decision level fusion based Gaussian mixture models (GMM), in which the (EM) and (GEM) algorithms for estimating the parameters of the mixture model and the number of mixture components have been compared. The test performance of the fusion, EER=0.0 % for "EM" and EER=0.02 % for "GEM", show that the combined behavioral information scheme is more robust and have a discriminating power, which can be explored for identity authentication.
关键词：Biometric authentication; behavioral biometrics; speech analysis; signature;verification; soft decision fusion and Gaussian Mixture Modal