\dm_csml_event_details UCL ELLIS

Quantifying Fingerprint Evidence using Bayesian Alignment


Peter Forbes


Oxford, Department of Statistics


Friday, 14 February 2014






Malet Place Engineering 1.02

Event series

DeepMind/ELLIS CSML Seminar Series


Fingerprint evidence has long been considered infallible by courtrooms
worldwide. However, subjective human judgement plays a large role in
determining whether or not two fingerprints match, especially when
dealing with the blurry prints typical of crime scenes. Despite this
uncertainty, courtroom fingerprint evidence is always presented
categorically as a match or non-match. This leads to inflated
confidence in the forensic evidence, and sometimes to false
convictions. These false convictions have instigated a push within
the forensics community to present courtroom evidence as a likelihood
ratio€ rather than a categorical match. Before this is possible, a
standardized method for quantifying the strength of fingerprint
evidence needs to be developed. I am developing a Bayesian
hierarchical model where the feature points of fingerprints (called
minutiae) are represented using spatial Poisson point processes.

Determining how well two fingerprints match reduces to identifying a
matching (a bipartite graph which determines which minutiae are common
to both fingerprints), and a rigid motion between the fingerprint
images such that the common minutiae are spatially close. An MCMC
algorithm has been developed to sum over all possible matchings and
rigid motions to determine the likelihood ratio between the
prosecution hypothesis (the two observed fingerprints originate from
the same finger) and the defense hypothesis (the two observed
fingerprints originate from independent fingers). The model has been
tested on a small database of 258 forensic fingerprints provided by
the American Forensic Bureau of Investigation.

Joint work with Steffen Lauritzen (Oxford) and Jesper Møller (Aalborg).

Slides for the talk: PDF