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                            Library & Multimedia
 Machine learning for signature verification
NCJ Number
              305964

Author(s)
          Harish Srinivasan;
                        Sargur N. Srihari;
                        Matthew J. Beal
Date Published

2006
Length
              17 pages
Annotation
              This paper describes the authors’ development and testing of a machine-learning technique for determining the authenticity of the signature on a document.
Abstract
              Signature verification is a common task in forensic document analysis. It is one of determining whether a questioned signature matches known signature samples. From the viewpoint of automating the task it can be viewed as one that involves machine learning from a population of signatures. There are two types of learning to be accomplished. In the first, the training set consists of “genuines” and forgeries from a general population. In the second there are genuine signatures in a given case. The two learning tasks are called person-independent (or general) learning and person-dependent (or special) learning. General learning is from a population of genuine and forged signatures of several individuals, where the differences between genuines and forgeries across all individuals are learned. The general learning model allows a questioned signature to be compared to a single genuine signature. In special learning, a person’s signature is learned from multiple samples of only that person’s signature– where within-person similarities are learned. When a sufficient number of samples are available, special learning performs better than general learning (5% higher accuracy). With special learning, verification accuracy increases with the number of samples. (Published abstract provided)
      
  
          Additional Details
Grant Number(s)
        2004-IJ-CX-K030


  
  
        
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        NIJ-Sponsored


  
  
        
  Award
        PSTC: The Florida Law Enforcement Consortium: An Affordable Data Sharing Model
    
  
  
Date Published: January 1, 2006
    
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        Questioned documents analysis



  
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