Fundamentals of speech recognition
Fundamentals of speech recognition
Reliable On-Line Human Signature Verification Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Visual Identification by Signature Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Multibiometrics (International Series on Biometrics)
Handbook of Multibiometrics (International Series on Biometrics)
Visual-Based Online Signature Verification by Pen Tip Tracking
CIMCA '08 Proceedings of the 2008 International Conference on Computational Intelligence for Modelling Control & Automation
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
An on-line signature verification system based on fusion of local and global information
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
A Markov chain Monte Carlo algorithm for bayesian dynamic signature verification
IEEE Transactions on Information Forensics and Security
Visual-based online signature verification using features extracted from video
Journal of Network and Computer Applications
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Fusion is a promising strategy to improve performance in biometrics, and many fusion methods have been proposed. Most of them are user-generic fusion strategies, because generating user-specific strategies for each user is difficult. In this paper, we propose an online signature verification method using a user-specific global-parameter fusion model. The basic fusion model is a user-generic (global-parameter) fusion model, but by introducing a user-dependent mean vector, we can generate a user-specific fusion model. To evaluate the proposed algorithm, several experiments were performed by using three public databases. The proposed algorithm yielded equal error rates (EERs) of 4.0%, 8.6%, and 6.1% for the MCYT, SVC2004 task2, and MyIDea databases, respectively.