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Proceedings Paper

Combining evidence using likelihood ratios in writer verification
Author(s): Sargur Srihari; Dimitry Kovalenko; Yi Tang; Gregory Ball
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Paper Abstract

Forensic identification is the task of determining whether or not observed evidence arose from a known source. It involves determining a likelihood ratio (LR) – the ratio of the joint probability of the evidence and source under the identification hypothesis (that the evidence came from the source) and under the exclusion hypothesis (that the evidence did not arise from the source). In LR- based decision methods, particularly handwriting comparison, a variable number of input evidences is used. A decision based on many pieces of evidence can result in nearly the same LR as one based on few pieces of evidence. We consider methods for distinguishing between such situations. One of these is to provide confidence intervals together with the decisions and another is to combine the inputs using weights. We propose a new method that generalizes the Bayesian approach and uses an explicitly defined discount function. Empirical evaluation with several data sets including synthetically generated ones and handwriting comparison shows greater flexibility of the proposed method.

Paper Details

Date Published: 4 February 2013
PDF: 11 pages
Proc. SPIE 8658, Document Recognition and Retrieval XX, 865807 (4 February 2013); doi: 10.1117/12.2002458
Show Author Affiliations
Sargur Srihari, Univ. at Buffalo, SUNY (United States)
Dimitry Kovalenko, Univ. at Buffalo, SUNY (United States)
Yi Tang, Univ. at Buffalo, SUNY (United States)
Gregory Ball, Univ. at Buffalo, SUNY (United States)


Published in SPIE Proceedings Vol. 8658:
Document Recognition and Retrieval XX
Richard Zanibbi; Bertrand Coüasnon, Editor(s)

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