Evidence evaluation in fingerprint comparison and automated fingerprint identification systems—Modelling within finger variability

Egli, Nicole; Champod, Christophe; Margot, Pierre

doi:10.1016/j.forsciint.2006.06.054

Cited by 80 publications

(57 citation statements)

References 5 publications

(6 reference statements)

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“…Since the LR method in our case consists of modelling the SS and DS score distributions, it is referred to as a LR model from here on. A detailed description of the LR model used -derived from [6] -is beyond the scope of this article, since the aim is to present the validation methodology with the focus on the analysis of coherence.…”

Section: Measuring Coherence: Experimental Example With Lrs Inferred mentioning

confidence: 99%

“…Having defined the set of propositions with respect to which the comparison scores are evaluated, we proceed to build the LR model [6]:…”

Section: Measuring Coherence: Experimental Example With Lrs Inferred mentioning

confidence: 99%

“…An increasingly adopted approach [1] uses a logical framework based on Bayes' Theorem to report forensic evidence in terms of likelihood ratios [1,2]. Computer-assisted LR methods (also referred to simply as LR methods), have been developed to assist the forensic practitioner in his role of forensic evaluator [3,4,5,6,7,8,9]. In these methods pattern recognition algorithms are often used for the feature extraction (analysis), the feature comparison, and statistical models are used for the evaluation of the forensic findings.…”

Section: Introductionmentioning

confidence: 99%

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Measuring coherence of computer-assisted likelihood ratio methods

Haraksim

Ramos

Meuwly

et al. 2015

Forensic Science International

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Measuring the performance of forensic evaluation methods that compute likelihood ratios (LRs) is relevant for both the development and the validation of such methods. A framework of performance characteristics categorized as primary and secondary is introduced in this study to help achieve such development and validation. Groundtruth labelled fingerprint data is used to assess the performance of an example likelihood ratio method in terms of those performance characteristics. Discrimination, calibration, and especially the coherence of this LR method are assessed as a function of the quantity and quality of the trace fingerprint data. Assessment of the coherence revealed a weakness of the comparison algorithm in the computer-assisted likelihood ratio method used. ABSTRACTMeasuring the performance of forensic evaluation methods that compute likelihood ratios (LRs) is relevant for both the development and the validation of such methods. A framework of performance characteristics categorized as primary and secondary is introduced in this study to help achieve such development and validation. Ground-truth labelled fingerprint data is used to assess the performance of an example likelihood ratio method in terms of those performance characteristics. Discrimination, calibration, and especially the coherence of this LR method are assessed as a function of the quantity and quality of the trace fingerprint data.Assessment of the coherence revealed a weakness of the comparison algorithm in the computer-assisted likelihood ratio method used.

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Section: Measuring Coherence: Experimental Example With Lrs Inferred mentioning

confidence: 99%

“…Having defined the set of propositions with respect to which the comparison scores are evaluated, we proceed to build the LR model [6]:…”

Section: Measuring Coherence: Experimental Example With Lrs Inferred mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Measuring coherence of computer-assisted likelihood ratio methods

Haraksim

Ramos

Meuwly

et al. 2015

Forensic Science International

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“…This gives all the information now required to compute the posterior likelihood that the suspect is the perpetrator. The LR has been recommended as a means of presenting forensic evidence in an understandable manner [5,[11][12][13]. Indeed, most treatments of forensic applications of the Bayesian approach prefer to avoid the controversial area of prior probabilities, i.e.…”

Section: Nature Of the Problem: Challenges In Identificationmentioning

confidence: 99%

Exact solutions to Bayesian and maximum likelihood problems in facial identification when population and error distributions are known

Allen¹

2008

Forensic Science International

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The reliability of traditional photogrammetric identification techniques using a small number of facial landmarks has recently come in for criticism. However, the transformation of parameters into a new face space in which the error distributions are orthogonal, yields a maximum likelihood solution to the problem of identifying a photographed face from a small, known, population which, in a simulated example, raises the success rate from 20% to 93%. A full transformation yielding simultaneously independent population and error distributions can be derived from raw population and error data using a straightforward computer procedure. Such a transformation facilitates computations for the situation where a single suspect is held in custody and the likelihood ratio of his being identical with a photograph is desired. It seems premature to condemn photogrammetry until the more efficient data-analysis approach outlined in this paper has been applied and tested. #

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“…Figure 1 shows an example of the three different types of fingerprints. Automated Fingerprint Identification Systems (AFIS) allow the search of the fingermarks among millions of tenprint cards usually producing a ranked list of top candidates based on similarity scores [7].…”

Section: Introductionmentioning

confidence: 99%