Probabilistic Measure for Signature Verification Based on Bayesian Learning
| Autor: | Sargur N. Srihari, Danjun Pu |
|---|---|
| Rok vydání: | 2010 |
| Předmět: |
education.field_of_study
business.industry Computer science Population Bayesian probability Posterior probability Feature extraction Probabilistic logic Pattern recognition computer.software_genre Bayesian inference Signature (logic) Likelihood-ratio test Probability distribution Artificial intelligence Data mining education business computer |
| Zdroj: | ICPR |
| DOI: | 10.1109/icpr.2010.1142 |
| Popis: | Signature verification is a common task in forensic document analysis. The goal is to make a decision whether a questioned signature belongs to a set of known signatures of an individual or not. In a typical forgery case a very limited number of known signatures may be available, with as few as four or five knowns \cite{Stev95}. Here we describe a fully Bayesian approach which overcomes the limitation of having too few genuine samples. The algorithm has three steps: Step 1: Learn prior distributions of parameters from a population of known signatures; Step 2: Determine the posterior distributions of parameters using the genuine samples of a particular person; Step 3: Determine probabilities of the query from both genuine and forgery classes and the Log Likelihood Ratio (LLR) of the query. Rather than give a hard decision, this method provides a probabilistic measure LLR of the decision and the performance of the Bayesian Learning is improved especially in the case of limited known samples. |
| Databáze: | OpenAIRE |
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