Abstract
In this paper, an approach based on the combination of discrete Hidden Markov Models (HMMs) in the ROC space is proposed to improve the performance of off-line signature verification (SV) systems designed from limited and unbalanced training data. This approach is inspired by the multiple-hypothesis principle, and allows the system to choose, from a set of different HMMs, the most suitable solution for a given input sample. By training an ensemble of user-specific HMMs with different number of states, and then combining these models in the ROC space, it is possible to construct a composite ROC curve that provides a more accurate estimation of system's performance during training and significantly reduces the error rates during operations. The experiments performed by using a real-world SV database with random, simple and skilled forgeries, indicated that the proposed approach can reduce the average error rates by more than 17%.
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