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From Biometric Scores to Forensic Likelihood Ratios
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The lower the LRs curve in an ECE plot, the more accurate the method.
“The lower this curve, the more accurate the method.”
From Calibration of score based likelihood ratio estimation in automated forensic facial image comparison (2022) · cites this paper as [39]
The solid curve in an ECE plot should always be lower than the reference curve, for all represented values of the prior log-odds.
“Therefore, the solid curve in an ECE plot should be always be lower than the reference curve, for all represented values of the prior log-odds.”
From Calibration of score based likelihood ratio estimation in automated forensic facial image comparison (2022) · cites this paper as [39]
The PAV LRs curve shows the best possible ECE in terms of calibration, and it is a measure of discriminating power.
“This shows the best possible ECE in terms of calibration, and it is a measure of discriminating power.”
From Calibration of score based likelihood ratio estimation in automated forensic facial image comparison (2022) · cites this paper as [39]
The reference curve represents the comparative performance of a neutral LR method, defined as the one which always delivers LR = 1 for each forensic case in the set of LR values.
“This curve represents the comparative performance of a so-called neutral LR method, defined as the one which always delivers LR = 1 for each forensic case in the set of LR values.”
From Calibration of score based likelihood ratio estimation in automated forensic facial image comparison (2022) · cites this paper as [39]
In the literature, such calibrations are performed normally as suspect-anchored and trace-anchored, however this type of calibration was not the use case in this study due to only having one sample of each identity in the comparison tests.
“In the literature, such calibrations are performed normally as suspect-anchored and trace-anchored [16,39], however this type of calibration was not the use case in this study due to only having one sample of each identity in the comparison tests.”
From Calibration of score based likelihood ratio estimation in automated forensic facial image comparison (2022) · cites this paper as [39]
The accuracy of an LR method should always be better than the neutral reference.
“This neutral method is taken as baseline performance: the accuracy should always be better than the neutral reference.”
From Calibration of score based likelihood ratio estimation in automated forensic facial image comparison (2022) · cites this paper as [39]
Minimizing Cllr implies an improvement of both discrimination and calibration performance of the automated system.
“Minimizing the value of Cllr implies an improvement of both discrimination and calibration performance of the automated system [39].”
From Calibration of score based likelihood ratio estimation in automated forensic facial image comparison (2022) · cites this paper as [39]
Citations
Cites
No references could be resolved yet.
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Cited by
Sorted by citations
Calibration of score based likelihood ratio estimation in automated forensic facial image comparison
Rodriguez, A. M., Geradts, Z., & Worring, M. (2022) · Forensic Science International · doi:10.1016/j.forsciint.2022.111239 · Cited by 19
Metadata
- Authors
- Ramos, D., Krish, R. P., Fierrez, J., & Meuwly, D.
- Venue
- Advances in Computer Vision and Pattern Recognition
- Publisher
- Springer International Publishing
- Published
- 2017
- Pages
- 305-327
- ISSN
- 2191-6586
- 2191-6594
- Cited by
- 18
- References
- 0
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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