The dramatic rise in identity theft, the ever pressing need to provide convenience in checkout services to attract and retain loyal customers, and the growing use of multi-function signature captures devices in the retail sector provides favorable conditions for the deployment of dynamic signature verification (DSV) in retail settings. We report on the development of a DSV system to meet the needs of the retail sector. We currently have a database of approximately 10,000 signatures collected from 600 subjects and forgers. Previous work at IBM on DSV has been merged and extended to achieve robust performance on pen position data available from commercial point of sale hardware, achieving equal error rates on skilled forgeries and authentic signatures of 1.5% to 4%.
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%.
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