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The complex jagged trajectory of fractured surfaces of two pieces of forensic evidence is used to recognize a "match" by using comparative microscopy and tactile pattern analysis. The material intrinsic properties and microstructures, as well as the exposure history of external forces on a fragment of forensic evidence have the premise of uniqueness at a relevant microscopic length scale (about 2–3 grains for cleavage fracture), wherein the statistics of the fracture surface become non-self-affine. We utilize these unique features to quantitatively describe the microscopic aspects of fracture surfaces for forensic comparisons, employing spectral analysis of the topography mapped by three-dimensional microscopy. Multivariate statistical learning tools are used to classify articles and result in near-perfect identification of a "match" and "non-match" among candidate forensic specimens. The framework has the potential for forensic application across a broad range of fractured materials and toolmarks, of diverse texture and mechanical properties. The complex jagged trajectory of fractured surfaces of two pieces of forensic evidence is used to recognize a match by using comparative microscopy. Here the authors leveraged statistical analysis of the surface topography to provide a quantitative description of the fracture surface, enabling forensic article comparisons with quantified probabilities. [ABSTRACT FROM AUTHOR]

Perceptual tasks such as object matching, mammogram interpretation, mental rotation, and satellite imagery change detection often require the assignment of correspondences to fuse information across views. We apply techniques developed for machine translation to the gaze data recorded from a complex perceptual matching task modeled after fingerprint examinations. The gaze data provide temporal sequences that the machine translation algorithm uses to estimate the subjects' assumptions of corresponding regions. Our results show that experts and novices have similar surface behavior, such as the number of fixations made or the duration of fixations. However, the approach applied to data from experts is able to identify more corresponding areas between two prints. The fixations that are associated with clusters that map with high probability to corresponding locations on the other print are likely to have greater utility in a visual matching task. These techniques address a fundamental problem in eye tracking research with perceptual matching tasks: Given that the eyes always point somewhere, which fixations are the most informative and therefore are likely to be relevant for the comparison task? (Contains 5 figures and 1 table.)

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. [ABSTRACT FROM AUTHOR]

The author emphasizes the importance of quality and quantity of information for forensic individualization of images. This piece is a reprint of the article "Forensic Individualization of Images Using Quality and Quantity of Information" by John R. Vanderkolk, which appeared in the "Journal of Forensic Identification" in 1999. He explains the factors to consider in understanding the source of an image. He also discusses how to analyze, compare and evaluate images.

Searching against larger Automated Fingerprint Identification System (AFIS) databases may increase the likelihood of finding a suspect in the database. However, Dror and Mnookin (2010) have argued that this also leads to an increase in the number of similar non-matching prints, which could lead to an erroneous identification. Using simulations, we explore the relation between database size and two outcome factors: close non-matching prints and overall database sensitivity, which is a measure of discriminability between true matches and close non-matches. We find that larger databases tend to increase both the likelihood of finding the suspect in the database as well as the number of close non-matching prints. However, the former tends to asymptote while the latter increases without bound, and this leads to an initial increase and then a decrease in the sensitivity of the database as more prints are added. This suggests the existence of an optimal database size, and that caution should be observed when interpreting results from larger databases. Quantitative evidentiary techniques such as likelihood ratios have the potential to address some of these concerns, although they too must consider the database size when calculating the likelihood ratio. Implications for practitioners are discussed. [ABSTRACT FROM PUBLISHER]

We recorded the eye positions of 18 expert latent print examiners and 18 novice participants across two separate experiments that were designed to represent abbreviated latent print examinations. In the first experiment, participants completed self-paced latent and inked comparisons presented on a computer monitor while their eyes were tracked with a commercial eye tracker. The similarity of eye fixation patterns was computed for each group of subjects. We found greater variability under some conditions among the experts than the novices in terms of the locations visited. However, experts spent approximately 50% longer than novices inspecting the images, which may have led to differences in strategies adopted by the two groups. A second experiment used trials that always lasted 20 seconds and found that under these time-controlled circumstances, experts were more consistent as a group than novices. Experts also had higher accuracy, spent a greater proportion of time inspecting the latent prints, and had shorter saccades than novices. However, the two groups spent an equal time looking at regions that contained minutiae. The results are generally consistent with experts relying on a common set of features that they choose to move their gaze to under time-limited conditions. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented in response to the article related to fingerprinting of identical twins in a 2013 issue.

Errors are generally not thought of as a positive thing - not in society at large, and especially not in forensic science. However, there is a large body of literature in the field of cognitive science (particularly from psychology and education research) that highlights the benefits that can be gained from using errors made in training to improve learning. Although none of these studies was done directly in the forensic science disciplines, there are nonetheless lessons to be learned about how errors may most effectively be used to maximize their benefits to learning. This article presents an overview of the literature on learning from errors and suggests principles that may be of benefit to forensic science today, as well as suggesting areas where specific research may be of benefit to forensic science in the future., (© 2022 The Authors. Published by Elsevier B.V.)