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دسته بندی:
حقوق خصوصی - Private law
سال انتشار:
2020
عنوان انگلیسی مقاله:
Automated face recognition in forensic science: Review and perspectives
ترجمه فارسی عنوان مقاله:
تشخیص خودکار جهره در علم پزشکی قانونی: بررسی و چشم انداز
منبع:
Sciencedirect - Elsevier - Forensic Science International, 307 (2020) 110124. doi:10.1016/j.forsciint.2019.110124
نویسنده:
Maëlig Jacquet*, Christophe Champod
چکیده انگلیسی:
With recent technological innovations, the multiplication of captured images of criminal events has brought
the comparison of faces to the forefront of the judicial scene. Forensic face recognition has become a
ubiquitous tool to guide investigations, gather intelligence and provide evidence in court. However, its
reliability in court still suffers from the lack of methodological standardization and empirical validation,
notably whenusingautomatic systems,whichcompare imagesandgenerate a matchingscore.Although the
use of such systems increases drastically, it still requires more empirical studies based on adequate forensic
data (surveillance footage and identity documents) to become a reliable method to present evidence in
court. In this paper, we propose a review of the literature leading to the establishment of a methodological
workflow to develop a score-based likelihood-ratio computation model using a Bayesian framework.
Different approaches are proposed in the literature regarding the within-source and between-sources
variability distributions modelling. Depending on the data available, the modelling approach can be specific
to the case or generic. Generic approaches allow interpreting the score without any available images of the
suspect. Such model is henceforth harder to defend in court because the results are not anchored to the
suspect. To make sure the computed score-based LR is robust, we must assess the performance of the model
with two main characteristics: the discriminating power and the calibration state of the model. We hence
describe the main metrics (Equal Error Rate and Cost of log likelihood-ratio), and graphical representations
(Tippett plots, Detection Error Trade-off plot and Empirical Cross-Entropy plot) used to quantify and
visualize the performance characteristics.
Keywords: Facial comparison | Biometric system | Likelihood ratio | Score | Calibration
قیمت: رایگان
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