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• W2168272004 abstract "When, in a criminal case there are traces from a crime scene - e.g., finger marks or facial recordings from a surveillance camera - as well as a suspect, the judge has to accept either the hypothesis emph{$H_{p}$} of the prosecution, stating that the trace originates from the subject, or the hypothesis of the defense emph{$H_d$}, stating the opposite. The current practice is that forensic experts provide a degree of support for either of the two hypotheses, based on their examinations of the trace and reference data - e.g., fingerprints or photos - taken from the suspect. There is a growing interest in a more objective quantitative support for these hypotheses based on the output of biometric systems instead of manual comparison. However, the output of a score-based biometric system is not directly suitable for quantifying the evidential value contained in a trace. A suitable measure that is gradually becoming accepted in the forensic community is the Likelihood Ratio (LR) which is the ratio of the probability of evidence given emph{$H_p$} and the probability of evidence given emph{$H_d$}. In this paper we study and compare different score-to-LR conversion methods (called calibration methods). We include four methods in this comparative study: Kernel Density Estimation (KDE), Logistic Regression (Log Reg), Histogram Binning (HB), and Pool Adjacent Violators (PAV). Useful statistics such as mean and bias of the bootstrap distribution of emph{LRs} for a single score value are calculated for each method varying population sizes and score location." @default.
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• W2168272004 date "2012-05-01" @default.
• W2168272004 modified "2023-09-26" @default.
• W2168272004 title "A review of calibration methods for biometric systems in forensic applications" @default.
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