Translator Disclaimer
4 February 2013 Combining evidence using likelihood ratios in writer verification
Author Affiliations +
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.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sargur Srihari, Dimitry Kovalenko, Yi Tang, and Gregory Ball "Combining evidence using likelihood ratios in writer verification", Proc. SPIE 8658, Document Recognition and Retrieval XX, 865807 (4 February 2013);

Back to Top