A method for measuring the quality of friction skin impression evidence: Method development and validation

Swofford, H.; Champod, Christophe; Koertner, A.; Eldridge, Heidi; Salyards, Michael J.

doi:10.1016/j.forsciint.2021.110703

Cited by 14 publications

(6 citation statements)

References 7 publications

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“…For example, when 18 fingerprint examiners marked ridge events indicating they were “highly confident” in their existence over 300 marks, they were accurate approximately 96% of the time. These encodings were also in line with the measurements carried out using the DFIQI software [ 65 ].…”

Section: Friction Ridge Skin and Its Individualization Processmentioning

confidence: 57%

“…LQMetrics delivers a quality map and a series of quality scores including a probability of a rank-1 correspondence on AFIS. Swofford et al [ 65 ] proposed the DFIQI tool which measures the clarity of friction ridge features (locally), evaluates the quality of impressions (globally), and maps them to three distinct value scales (value, difficulty and complexity, as defined by Eldridge [ 66 , 67 ]). In an operational environment, the tool is intended to provide an empirical foundation to support experts' subjective judgments and to provide transparency to the overall quality of a given mark.…”

Section: Friction Ridge Skin and Its Individualization Processmentioning

confidence: 99%

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Interpol review of fingermarks and other body impressions (2019 – 2022)

Bécue

Champod

2023

Forensic Science International: Synergy

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Section: Friction Ridge Skin and Its Individualization Processmentioning

confidence: 57%

Section: Friction Ridge Skin and Its Individualization Processmentioning

confidence: 99%

Interpol review of fingermarks and other body impressions (2019 – 2022)

Bécue

Champod

2023

Forensic Science International: Synergy

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“…Their approach, however, does not consider second-level friction ridge features, such as minutiae points, often used in fingerprint recognition systems. Swofford et al [19] recently proposed an approach to assess the reliability of individual minutiae and combine those estimates into a global quality value. Like the LFIQ, our tests indicated that their method is sensitive to spurious minutiae and is, therefore, not suitable to be used in combination with automated minutiae extractors.…”

Section: Automated Fingermark Quality Assessment Methodsmentioning

confidence: 99%

Probabilistic Fingermark Quality Assessment with Quality Region Localisation

Oblak

Haraksim

Beslay

et al. 2023

Sensors

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The assessment of fingermark (latent fingerprint) quality is an intrinsic part of a forensic investigation. The fingermark quality indicates the value and utility of the trace evidence recovered from the crime scene in the course of a forensic investigation; it determines how the evidence will be processed, and it correlates with the probability of finding a corresponding fingerprint in the reference dataset. The deposition of fingermarks on random surfaces occurs spontaneously in an uncontrolled fashion, which introduces imperfections to the resulting impression of the friction ridge pattern. In this work, we propose a new probabilistic framework for Automated Fingermark Quality Assessment (AFQA). We used modern deep learning techniques, which have the ability to extract patterns even from noisy data, and combined them with a methodology from the field of eXplainable AI (XAI) to make our models more transparent. Our solution first predicts a quality probability distribution, from which we then calculate the final quality value and, if needed, the uncertainty of the model. Additionally, we complemented the predicted quality value with a corresponding quality map. We used GradCAM to determine which regions of the fingermark had the largest effect on the overall quality prediction. We show that the resulting quality maps are highly correlated with the density of minutiae points in the input image. Our deep learning approach achieved high regression performance, while significantly improving the interpretability and transparency of the predictions.

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“…In recent years, researchers have developed multiple quality metrics with the goal of objectively evaluating the quality and/or clarity of latent prints (for a brief review, see [22]). All quality metrics utilize algorithms that incorporate different aspects of prints to calculate a score (e.g., the number of features, ridge contrast, blur versus print clarity) and generally can be categorized as either a global or feature-specific metric.…”

Section: Print Quality Metricsmentioning

confidence: 99%

“…All quality metrics utilize algorithms that incorporate different aspects of prints to calculate a score (e.g., the number of features, ridge contrast, blur versus print clarity) and generally can be categorized as either a global or feature-specific metric. Global metrics such as the Latent Quality Metrics (LQMetrics) software within the FBI's Universal Latent Workstation (ULW) [8,24] and the Defense Fingerprint Image Quality Index (DFIQI) software [22] provide overall scores for the quality and clarity of an entire latent print. Feature-or minutiaespecific metrics provide individual scores for certain aspects of each print.…”

Section: Print Quality Metricsmentioning

confidence: 99%

Latent print quality in blind proficiency testing: Using quality metrics to examine laboratory performance

Gardner

Neuman

Kelley

2021

Forensic Science International

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Calls for blind proficiency testing in forensic science disciplines intensified following the 2009 National Academy of Sciences report and were echoed in the 2016 report by the President's Council of Advisors on Science and Technology. Both practitioners and scholars have noted that "open" proficiency tests, in which analysts know they are being tested, allow for test-taking behavior that is not representative of behavior in routine casework. This study reports the outcomes of one laboratory's blind quality control (BQC) program. Specifically, we describe results from approximately 2.5 years of blind cases in the latent print section (N = 376 latent prints submitted as part of 144 cases). We also used a widely available quality metrics software (LQMetrics) to explore relationships between objective print quality and case outcomes. Results revealed that nearly all BQC prints (92.0%) were of sufficient quality to enter into AFIS. When prints had a source present in AFIS, 41.7% of print searches resulted in a candidate list containing the true source. Examiners committed no false positive errors but other types of errors were more common. Average print quality was in the midpoint of the range (53.4 on a 0-to-100 scale), though prints were evenly distributed across the Good, Bad, and Ugly categories. Quality metrics were significantly associated with sufficiency determinations, examiner conclusions, and examiner accuracy. Implications for blind testing and the use of quality metrics in routine casework as well as proficiency testing are discussed.

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A method for measuring the quality of friction skin impression evidence: Method development and validation

Cited by 14 publications

References 7 publications

Interpol review of fingermarks and other body impressions (2019 – 2022)

Interpol review of fingermarks and other body impressions (2019 – 2022)

Probabilistic Fingermark Quality Assessment with Quality Region Localisation

Latent print quality in blind proficiency testing: Using quality metrics to examine laboratory performance

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