Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Floating search methods in feature selection
Pattern Recognition Letters
Reliable On-Line Human Signature Verification Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line Signature Verification with Hidden Markov Models
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
A writer identification system for on-line whiteboard data
Pattern Recognition
Promoting Diversity in Gaussian Mixture Ensembles: An Application to Signature Verification
Biometrics and Identity Management
Who dotted that 'i'?: context free user differentiation through pressure and tilt pen data
Proceedings of Graphics Interface 2009
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Feature Selection and Binarization for On-Line Signature Recognition
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Development of a Sigma-Lognormal representation for on-line signatures
Pattern Recognition
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
On-line signature verification based on spatio-temporal correlation
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
Ergodic HMM-UBM system for on-line signature verification
BioID_MultiComm'09 Proceedings of the 2009 joint COST 2101 and 2102 international conference on Biometric ID management and multimodal communication
Feature set selection for on-line signatures using selection of regression variables
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
A survey of on-line signature verification
CCBR'11 Proceedings of the 6th Chinese conference on Biometric recognition
Online signature verification method based on the acceleration signals of handwriting samples
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
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In this paper we propose a methodology for selecting the most discriminative features in a set for online signature verification. We expose the difference in the definition of class between signature verification and other pattern recognition tasks, and extend the classical Fisher ratio to make it more robust to the small sample sizes typically found when dealing with global features and client enrollment time constraints for signature verification systems. We apply our methodology to global and local features extracted from a 50-users database, and find that our criterion agrees better with classifier error rates for local features than for global features. We discuss the possibility of performing feature selection without having forgery data available.