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- W2159772502 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%." @default.
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- W2159772502 date "2009-01-01" @default.
- W2159772502 modified "2023-09-26" @default.
- W2159772502 title "A Multi-Hypothesis Approach for Off-Line Signature Verification with HMMs" @default.
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- W2159772502 doi "https://doi.org/10.1109/icdar.2009.5" @default.
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