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- W1550192466 abstract "A machine learning approach to off-line signature verification is presented. The prior distributions are determined from and forged signatures of several individuals. The task of signature verification is a problem of determining genuine-class membership of a questioned (test) signature. We take a 3-step, writer independent approach: 1) Determine the prior parameter distributions for means of both vs. genuine and vs. known classes using a distance metric. 2) Enroll n and m forgery signatures for a particular writer and calculate both the posterior class probabilities for both classes. 3) When evaluating a questioned signature, determine the probabilities for each class and choose the class with bigger probability. By using this approach, performance over other approaches to the same problem is dramatically improved, especially when the number of available signatures for enrollment is small. On the NISDCC dataset, when enrolling 4 signatures, the new method yielded a 12.1% average error rate, a significant improvement over a previously described Bayesian method." @default.
- W1550192466 created "2016-06-24" @default.
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- W1550192466 date "2009-01-01" @default.
- W1550192466 modified "2023-09-23" @default.
- W1550192466 title "A Machine Learning Approach to Off-Line Signature Verification Using Bayesian Inference" @default.
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- W1550192466 doi "https://doi.org/10.1007/978-3-642-03521-0_12" @default.
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