Forensic Comparison and Matching of Fingerprints: Using Quantitative Image Measures for Estimating Error Rates through Understanding and Predicting Difficulty

Abstract

Latent fingerprint examination is a complex task that, despite advances in image processing, still fundamentally depends on the visual judgments of highly trained human examiners. Fingerprints collected from crime scenes typically contain less information than fingerprints collected under controlled conditions. Specifically, they are often noisy and distorted and may contain only a portion of the total fingerprint area. Expertise in fingerprint comparison, like other forms of perceptual expertise, such as face recognition or aircraft identification, depends on perceptual learning processes that lead to the discovery of features and relations that matter in comparing prints. Relatively little is known about the perceptual processes involved in making comparisons, and even less is known about what characteristics of fingerprint pairs make particular comparisons easy or difficult. We measured expert examiner performance and judgments of difficulty and confidence on a new fingerprint database. We developed a number of quantitative measures of image characteristics and used multiple regression techniques to discover objective predictors of error as well as perceived difficulty and confidence. A number of useful predictors emerged, and these included variables related to image quality metrics, such as intensity and contrast information, as well as measures of information quantity, such as the total fingerprint area. Also included were configural features that fingerprint experts have noted, such as the presence and clarity of global features and fingerprint ridges. Within the constraints of the overall low error rates of experts, a regression model incorporating the derived predictors demonstrated reasonable success in predicting objective difficulty for print pairs, as shown both in goodness of fit measures to the original data set and in a cross validation test. The results indicate the plausibility of using objective image metrics to predict expert performance and subjective assessment of difficulty in fingerprint comparisons.

Discriminating fingermarks with evidential value for forensic comparison

“…Ulery et al show that accuracy and repeatability varies even for forensic experts and mostly depends on the print quality [6,7], especially for borderline decisions. Consequently, Kellman et al use image features to predict "expert performance and subjective assessment of difficulty in fingerprint comparisons" [8].…”

A Small Population Study on Friction Skin Ridges: Differences in Ridge Widths Between Latent and Inked Fingerprints

“…In this case, any potential ridge width data point outlier could be explained by a lower "quality" value of the print, caused either by the deposition technique, the visualization technique, and/or any unforeseen factors that could affect the image being examined. There have been studies on the ability of an examiner to assign a quality rating to fingerprints based on their level of skill (17)(18)(19). While there were many different factors that affected the quality rating of the print, this research showed that experts, while not perfect, are highly qualified and able to assign a consistent quality rating.…”

Fingerprint identification: advances since the 2009 National Research Council report

“…Such research opens the possibility of automatically distinguishing between complex marks and non-complex marks as suggested in the Fingerprint Inquiry report. Kellman et al [46] have also shown the possibility of using metrics characterizing the image of the mark to predict expert performance and the assessment of difficulty in fingerprint comparisons.…”