Quantifying the weight of fingerprint evidence through the spatial relationship, directions and types of minutiae observed on fingermarks

Neumann, Cédric; Champod, Christophe; Yoo, Mina; Genessay, Thibault; Langenburg, Glenn

doi:10.1016/j.forsciint.2015.01.007

Cited by 26 publications

(11 citation statements)

References 3 publications

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“…Control images are taken under conditions comparable to those of the query image [54] to be used as special references. They are also described as "pseudo-traces" in [54,58,59] in biometrics in general and more specifically for voices and fingerprints. When comparing facial images, the time between the recording of the trace and the references or control images often become problematic because of the ageing of the person.…”

Section: Suspect-anchored Approachmentioning

confidence: 99%

Automated face recognition in forensic science: Review and perspectives

Jacquet

Champod

2020

Forensic Science International

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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 when using automatic systems, which compare images and generate a matching score. Although the use of such systems increases drastically, it still requires more empirical studies based on adequate forensic data (surveillance footages 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-source 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 scorebased 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.

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Section: Suspect-anchored Approachmentioning

confidence: 99%

Automated face recognition in forensic science: Review and perspectives

Jacquet

Champod

2020

Forensic Science International

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“…[2][3][4] In the absence of alternatives, this all-or-nothing approach has been an effective, though imperfect compromise. [5] Methods to measure the associative value (selectivity) of fingerprints are currently under active development, [6][7][8][9][10][11][12][13][14] but are not yet sufficiently defined and vetted for widespread use and acceptance. However, we can be sure that such methods will not be long in coming.…”

Section: Introductionmentioning

confidence: 99%

Occurrence and associative value of non-identifiable fingermarks

Stoney

Donno

Champod

et al. 2020

Forensic Science International

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Fingermarks that have insufficient characteristics for identification often have discernible characteristics that could form the basis for lesser degrees of correspondence or probability of occurrence within a population. Currently, those latent prints that experts judge to be insufficient for identification are not used as associative evidence. How often do such prints occur and what is their potential value for association? The answers are important. We could be routinely setting aside a very important source of associative evidence, with high potential impact, in many cases; or such prints might be of very low utility, adding very little, or only very rarely contributing to cases in a meaningful way. The first step is to better understand the occurrence and range of associative value of these fingermarks.The project goal was to explore and test a theory that in large numbers of cases fingermarks of no value for identification purposes occur and are readily available, though not used, and yet have associative value that could provide useful information.Latent fingermarks were collected from nine state and local jurisdictions. Fingermarks included were those (1) collected in the course of investigations using existing jurisdictional procedures, (2) originally assessed by the laboratory as of no value for identification (NVID), (3) re-assessed by expert review as NVID, but with least three clear and reliable minutiae in relationship to one another, and (4) determined to show at least three auto-encoded minutiae.An expected associative value (ESLR) for each mark was measured, without reference to a putative source, based on modeling within-variability and between-variability of AFIS scores. This method incorporated (1) latest generation feature extraction, (2) a (minutiae-only) matcher, (3) a validated distortion model, and (4) NIST SD27 database calibration. Observed associative value distributions were determined for violent crimes, property crimes, and for existing objective measurements of latent print quality. 750 Non Identifiable Fingermarks (NIFMs) showed values of Log10 ESLR ranging from 1.05 to 10.88, with a mean value of 5.56 (s.d. 2.29), corresponding to an ESLR of approximately 380,000.It is clear that there are large numbers of cases where NIFMs occur that have high potential associative value as indicated by the ESLR. These NIFMs are readily available, but not used, yet have associative value that could provide useful information. These findings lead to the followon questions, "How useful would NIFM evidence be in actual practice?" and, "What developments or improvements are needed to maximize this contribution?"

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“…The results of this comparison stage are typically phrased in terms of an identification (implying that the two impressions came from the same finger), an exclusion (implying that two different fingers created the two impressions), or inconclusive (implying that no determination was possible). During most testimony, the decision is not accompanied by qualifications about confidence or difficulty, and virtually all evidence presented in court is by human experts without reference to a computer match or statistical support from a model of the distributions of features (although this may change; see Neumann et al., , ; Neumann, Champod, Yoo, Genessay, & Langenburg, ; Neumann, Evett, & Skerrett, ; Srihari & Su, ; Su & Srihari, ).…”

Section: Introductionmentioning

confidence: 99%

Characterizing Human Expertise Using Computational Metrics of Feature Diagnosticity in a Pattern Matching Task

et al. 2016

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Forensic evidence often involves an evaluation of whether two impressions were made by the same source, such as whether a fingerprint from a crime scene has detail in agreement with an impression taken from a suspect. Human experts currently outperform computer-based comparison systems, but the strength of the evidence exemplified by the observed detail in agreement must be evaluated against the possibility that some other individual may have created the crime scene impression. Therefore, the strongest evidence comes from features in agreement that are also not shared with other impressions from other individuals. We characterize the nature of human expertise by applying two extant metrics to the images used in a fingerprint recognition task and use eye gaze data from experts to both tune and validate the models. The Attention via Information Maximization (AIM) model (Bruce & Tsotsos, 2009) quantifies the rarity of regions in the fingerprints to determine diagnosticity for purposes of excluding alternative sources. The CoVar model (Karklin & Lewicki, 2009) captures relationships between low-level features, mimicking properties of the early visual system. Both models produced classification and generalization performance in the 75%-80% range when classifying where experts tend to look. A validation study using regions identified by the AIM model as diagnostic demonstrates that human experts perform better when given regions of high diagnosticity. The computational nature of the metrics may help guard against wrongful convictions, as well as provide a quantitative measure of the strength of evidence in casework.

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Quantifying the weight of fingerprint evidence through the spatial relationship, directions and types of minutiae observed on fingermarks

Cited by 26 publications

References 3 publications

Automated face recognition in forensic science: Review and perspectives

Automated face recognition in forensic science: Review and perspectives

Occurrence and associative value of non-identifiable fingermarks

Characterizing Human Expertise Using Computational Metrics of Feature Diagnosticity in a Pattern Matching Task

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