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Quantifying Uncertainty in Forensic Identification

Sargur N. Srihari
SUNY Distinguished Professor
University at Buffalo, The State University of New York, USA
srihari@cedar.buffalo.edu

Abstract

Forensic identification is the task of determining whether or not observed evidence arose from a known source.  The conclusion made by human forensic examiners  is a choice among three opinions: identification/exclusion/no-opinion. Due to several  court cases in which convictions have been over-turned due to the availability of  additional evidence, the court system (judges and juries) have begun to expect  a characterization of the confidence in the expressed forensic opinion.  Today, in most forensic domains outside of DNA, it is not possible to make a probability statement since the necessary  distributions cannot be computed with reasonable accuracy. This talk will describe methods for the evaluation  of 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. This is because the number of parameters  is exponential with the number of variables used to represent the evidence and therefore statistical modeling and inference become intractable.   We can 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 that results in significant inaccuracy.   We propose third method, which decomposes the LR into a product of two factors, one based on distance and the other on rarity. The method has intuitive appeal since forensic examiners assign higher importance to rare attributes in the evidence and is also the principal method used by search engines  in ranking web-pages.  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, footwear marks and fingerprints show that  the distance and rarity method is significantly better than the distance only method.

 

SARGUR N. SRIHARI
Biography

Sargur (Hari) Srihari is a SUNY Distinguished Professor in the Computer Science and Engineering Department at the State University of New York at Buffalo.  He is the founding director of CEDAR, the Center of Excellence for Document Analysis and Recognition, which was recognized as the first United State Postal Service Center of Excellence in 1991.  Research at CEDAR spawned a new thread of work in pattern recognition which led to the first Handwritten Address Interpretation (HWAI) system, the first name and address block reader (NABR) used by the IRS, and the first comprehensive forensic handwriting examination system.

Srihari has been a member of several national committees, including the Board of Scientific Counselors of the National Library of Medicine  for six years (2001-2007),  the National Academy of Sciences  Committee on Identifying the Needs of the Forensic Science Community (2006-2008),  the NIST Expert Working Group on Human Factors in Latent Print Analysis (2008-10), and the Houston Forensic Science LGC Technical Advisory Group (2013-15).

Srihari's honors include: Outstanding Achievements Award of IAPR/ICDAR in Beijing China in 2011, Fellow of the Institute of Electronics and Telecommunications Engineers (IETE, India) in 1992, Fellow of the Institute of Electrical and Electronics Engineers (IEEE)  in 1995, Fellow of the International Association for Pattern Recognition  in 1996 and distinguished alumnus of the Ohio State University College of Engineering  in 1999.

Srihari has served as principal adviser on 37 doctoral dissertations. He currently teaches courses on Machine Learning and on Probabilistic Graphical Models.

Srihari received a B.Sc. in Physics and Mathematics from the Bangalore University in 1967, a B.E. in Electrical Communication Engineering from the Indian Institute of Science, Bangalore  in 1970, and a Ph.D. in Computer and Information Science  from the Ohio State University, Columbus in 1976.