Probabilistic Measure for Signature Verification Based on Bayesian Learning
Danjun Pu, Sargur N. Srihari · 2010
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 knows. 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.