“…Likelihood ratios of evidence depend on the choice of scenarios, or hypotheses, that are considered . In LR systems for fingerprint evidence that are currently proposed in the literature such as , the hypotheses typically are at the level of the finger: H a : The fingermark and the fingerprint originate from the same finger, H b : The fingermark and the fingerprint do not originate from the same finger.…”
In this article, the performance of a score-based likelihood ratio (LR) system for comparisons of fingerprints with fingermarks is studied. The system is based on an automated fingerprint identification system (AFIS) comparison algorithm and focuses on fingerprint comparisons where the fingermarks contain 6-11 minutiae. The hypotheses under consideration are evaluated at the level of the person, not the finger. The LRs are presented with bootstrap intervals indicating the sampling uncertainty involved. Several aspects of the performance are measured: leave-one-out cross-validation is applied, and rates of misleading evidence are studied in two ways. A simulation study is performed to study the coverage of the bootstrap intervals. The results indicate that the evidential strength for same source comparisons that do not meet the Dutch twelve-point standard may be substantial. The methods used can be generalized to measure the performance of score-based LR systems in other fields of forensic science.
“…Likelihood ratios of evidence depend on the choice of scenarios, or hypotheses, that are considered . In LR systems for fingerprint evidence that are currently proposed in the literature such as , the hypotheses typically are at the level of the finger: H a : The fingermark and the fingerprint originate from the same finger, H b : The fingermark and the fingerprint do not originate from the same finger.…”
In this article, the performance of a score-based likelihood ratio (LR) system for comparisons of fingerprints with fingermarks is studied. The system is based on an automated fingerprint identification system (AFIS) comparison algorithm and focuses on fingerprint comparisons where the fingermarks contain 6-11 minutiae. The hypotheses under consideration are evaluated at the level of the person, not the finger. The LRs are presented with bootstrap intervals indicating the sampling uncertainty involved. Several aspects of the performance are measured: leave-one-out cross-validation is applied, and rates of misleading evidence are studied in two ways. A simulation study is performed to study the coverage of the bootstrap intervals. The results indicate that the evidential strength for same source comparisons that do not meet the Dutch twelve-point standard may be substantial. The methods used can be generalized to measure the performance of score-based LR systems in other fields of forensic science.
“…While it is true that a larger database may make a match more likely, this also means that more individuals have been shown to be excluded as non-contributors. However, it is generally not the domain of the forensic scientist to introduce prior odds into the case deliberations (Sjerps and Meester 2009;Biedermann et al 2011;Nordgaard et al 2012). It is the role of the court to evaluate additional non-DNA factors in each case.…”
“…Its implications in the practice of criminal proceedings span a wide range. The debate was led essentially in the context of DNA evidence, but the underlying principle of searching databases containing analytical characteristics that serve as a basis for comparative forensic examinations applies also to other kinds or categories of scientific evidence [9]. Although this problem is strongly rooted in practical applications, deciding on an appropriate approach to deal with this inference problem requires coherent methodological developments.…”
BackgroundThe ‘database search problem’, that is, the strengthening of a case - in terms of probative value - against an individual who is found as a result of a database search, has been approached during the last two decades with substantial mathematical analyses, accompanied by lively debate and centrally opposing conclusions. This represents a challenging obstacle in teaching but also hinders a balanced and coherent discussion of the topic within the wider scientific and legal community. This paper revisits and tracks the associated mathematical analyses in terms of Bayesian networks. Their derivation and discussion for capturing probabilistic arguments that explain the database search problem are outlined in detail. The resulting Bayesian networks offer a distinct view on the main debated issues, along with further clarity.MethodsAs a general framework for representing and analyzing formal arguments in probabilistic reasoning about uncertain target propositions (that is, whether or not a given individual is the source of a crime stain), this paper relies on graphical probability models, in particular, Bayesian networks. This graphical probability modeling approach is used to capture, within a single model, a series of key variables, such as the number of individuals in a database, the size of the population of potential crime stain sources, and the rarity of the corresponding analytical characteristics in a relevant population.ResultsThis paper demonstrates the feasibility of deriving Bayesian network structures for analyzing, representing, and tracking the database search problem. The output of the proposed models can be shown to agree with existing but exclusively formulaic approaches.ConclusionsThe proposed Bayesian networks allow one to capture and analyze the currently most well-supported but reputedly counter-intuitive and difficult solution to the database search problem in a way that goes beyond the traditional, purely formulaic expressions. The method’s graphical environment, along with its computational and probabilistic architectures, represents a rich package that offers analysts and discussants with additional modes of interaction, concise representation, and coherent communication.
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