Invited Lecture II: Evaluating the Probability of Identification in the Forensic Sciences

Forensic identification is the task of determining whether or not observed evidence arose from a known source. In every forensic domain, it is useful to determine the probability that the evidence and known can be attributed to the same individual so that identification/exclusion opinions can be accompanied by a probability statement. At present, in most forensic domains outside of DNA, it is not possible to make such a statement since the necessary probability distributions cannot be computed with reasonable accuracy. 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). The joint probability approach is computationally and statistically infeasible when the number of variables is even moderately large, e.g., the number of parameters to be determined is exponential with the number of variables. An approximate method is to replace the joint probability by another probability: that of (dis)imilarity between evidence and object under the two hypotheses. While this distance-based approach reduces to linear complexity with the number of variables, it is an oversimplification. A third method, which decomposes the LR into a product of two factors, one based on distance and the other on rarity, has intuitive appeal-forensic examiners assign higher importance to rare attributes in the evidence. Theoretical discussions of the three approaches and empirical evaluations done with several data types (continuous features, binary features, multinomial and graph) will be described. Experiments with handwriting using binary and multinomial features show that the distance and rarity method is significantly better than the distance only method. Work was done with Yi Tang.