Algorithms for clustering data
Algorithms for clustering data
Minimum Classification Error Training for Online Handwritten Word Recognition
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Minimum classification error training of hidden Markov models for handwriting recognition
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
A writer identification system for on-line whiteboard data
Pattern Recognition
A general approach to off-line signature verification
WSEAS Transactions on Computers
Classification approaches in off-line handwritten signature verification
WSEAS Transactions on Mathematics
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In this paper we describe a new approach to dynamic signature verification using the discriminative training framework. The authentic and forgery samples are represented by two separate Gaussian Mixture models and discriminative training is used to achieve optimal separation between the two models. An enrollment sample clustering and screening procedure is described which improves the robustness of the system. We also introduce a method to estimate and apply subject norms representing the "typical": variation of the subject's signatures. The subject norm functions are parameterized, and the parameters are trained as an integral part of the discriminative training. The system was evaluated using 480 authentic signature samples and 260 skilled forgery samples from 44 accounts and achieved an equal error rate of 2.25%.