Universal Background Models for Dynamic Signature Verification

The applicability of universal background models as a score normalization technique is studied for the case of dynamic signature verification. This technique is commonly used in speaker verification systems. Background models are tested in two different systems based on global features: one based on Parzen windows and another based on adapted Gaussian mixture models. Experiments are carried out in the large MCYT database (16,500 signatures from 330 users) revealing a significant improvement in the overall system performance, specially in the casual impostor scenario.

[1]  J. Richiardi,et al.  Gaussian Mixture Models for on-line signature verification , 2003, WBMA '03.

[2]  Juan J. Igarza,et al.  MCYT baseline corpus: a bimodal biometric database , 2003 .

[3]  Sharath Pankanti,et al.  Biometrics: a tool for information security , 2006, IEEE Transactions on Information Forensics and Security.

[4]  Jonas Richiardi,et al.  Local and global feature selection for on-line signature verification , 2005, Eighth International Conference on Document Analysis and Recognition (ICDAR'05).

[5]  Arun Ross,et al.  Score normalization in multimodal biometric systems , 2005, Pattern Recognit..

[6]  Loris Nanni,et al.  An On-Line Signature Verification System Based on Fusion of Local and Global Information , 2005, AVBPA.

[7]  Réjean Plamondon,et al.  Automatic signature verification and writer identification - the state of the art , 1989, Pattern Recognit..

[8]  Douglas A. Reynolds,et al.  A Tutorial on Text-Independent Speaker Verification , 2004, EURASIP J. Adv. Signal Process..

[9]  Michael C. Fairhurst,et al.  Biosecure reference systems for on-line signature verification: A study of complementarity , 2007, Ann. des Télécommunications.

[10]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[11]  Douglas A. Reynolds,et al.  Speaker Verification Using Adapted Gaussian Mixture Models , 2000, Digit. Signal Process..

[12]  Sargur N. Srihari,et al.  On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Douglas A. Reynolds,et al.  Comparison of background normalization methods for text-independent speaker verification , 1997, EUROSPEECH.

[14]  Julian Fiérrez,et al.  Target dependent score normalization techniques and their application to signature verification , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).