Wavelet-based off-line handwritten signature verification
Computer Vision and Image Understanding
Learning Strategies and Classification Methods for Off-Line Signature Verification
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Identity authentication using improved online signature verification method
Pattern Recognition Letters
Spectrum Analysis Based onWindows with Variable Widths for Online Signature Verification
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
On-line signature recognition based on VQ-DTW
Pattern Recognition
Symbolic Representation of On-line Signatures
ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 02
A novel local on-line signature verification system
Pattern Recognition Letters
Relative Orientations of Geometric Centroids for Off-line Signature Verification
ICAPR '09 Proceedings of the 2009 Seventh International Conference on Advances in Pattern Recognition
Practical On-Line Signature Verification
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Automatic signature verification based on accelerometry
IBM Journal of Research and Development
Online signature verification using Fourier descriptors
EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in biometric systems: a signal processing perspective
Signature verification based on a global classifier that uses universal forgery features
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part II
Automatic Signature Verification: The State of the Art
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
On Using the Viterbi Path Along With HMM Likelihood Information for Online Signature Verification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IJCB '11 Proceedings of the 2011 International Joint Conference on Biometrics
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Handwritten signature verification algorithms are designed to distinguish between genuine signatures and forgeries. One of the central issues with such algorithms is the unavailability of skilled forgeries during the template creation. As a solution, we propose the idea of universalforgery features, where a global classifier is used to classify a signature as a genuine one or as a forgery, without the actual knowledge of the signature template and its owner. This classifier is trained once, during the system tuning on a group of historical data. A global classifier trained on a set of training signatures is not to be additionally trained after implementation; in other words, additional user enrollments have no effect on the global classifier parameters. This idea effectively solves the issue of the lack of skilled forgeries during template creation. We show that this approach can be applied both in on-line and off-line signature verification systems.