The impact of fatigue on latent print examinations as revealed by behavioral and eye gaze testing

Busey, Thomas A.; Swofford, H.; Vanderkolk, John R.; Emerick, Brandi

doi:10.1016/j.forsciint.2015.03.028

Cited by 20 publications

(27 citation statements)

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“…The experiment was conducted online using JavaScript to present the images and PHP/MySQL to save the data. Despite the addition of noise, the images contain a large amount of detail, and prior studies with similar images and unconstrained viewing produced very high accuracy values (Busey et al., , , ). Because the goal was to compare among the three types of trials (best regions, worst regions or all regions), we need to have accuracy below ceiling.…”

Section: Resultsmentioning

confidence: 99%

“…Finally, to encourage examiners to treat the impressions as similar to actual latent prints, we combined the images with noise sampled from an actual latent print. This combination was done using a multiplicative process that mimics the subtractive nature of physical noise (Busey, Swofford, Vanderkolk, & Emerick, ). These images were then paired with either a different impression from the same finger or a similar non‐mated pair.…”

Section: Resultsmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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

et al. 2016

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“…A variety of studies have evaluated the latent print examination process (e.g., Hicklin et al, 2011;Ulery, Hicklin, Buscaglia, & Roberts, 2011Ulery, Hicklin, Roberts, & Buscaglia, 2014a, 2014b, but relatively little research has addressed the fundamental basis of this visual task: how examiners use their eyes to accomplish these tasks and what visual information guides the examination process (Busey et al, 2011;Busey, Swofford, Vanderkolk, & Emerick, 2015;Busey, Yu, Wyatte, & Vanderkolk, 2013;Parada et al, 2015). This presentation will discuss the results of a study conducted to gain a greater understanding of how latent print examiners perform analysis and comparison tasks and of why examiners make different determinations.…”

Section: Using Eye Tracking To Understand Decisions By Forensic Latenmentioning

confidence: 99%

2018 Impression, Pattern and Trace Evidence Symposium

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2018

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“…Eye behavior is constrained by the small features (minutiae), the narrow visual field of the fovea, and the capacity and persistence of visual working memory. The process of finding an area that corresponds to a selected target group typically requires multiple fixations on the latent impression, and then multiple fixations on the exemplar impression (Busey et al, 2011; Busey, Yu, Wyatte, & Vanderkolk, 2013; Busey, Swofford, Vanderkolk, & Emerick, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Natural scenes may serve as a good model for fingerprints because they also contain structural information that provides context that guides the interpretation of individual features. Of the few studies that have addressed the eye-gaze behavior of fingerprint examiners (Busey et al, 2011, 2013, 2015), none has addressed the effect of context. However, working with structured displays, Chun and Jiang (1998, 1999) found that spatial configuration information can indirectly guide search to the target because the visual covariation of the structure of a display is learned; similar experience may help fingerprint examiners infer the general location of a region from a partial print (e.g., deltas are generally found below the core).…”

Section: Introductionmentioning

confidence: 99%

Gaze behavior and cognitive states during fingerprint target group localization

et al. 2019

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Background The comparison of fingerprints by expert latent print examiners generally involves repeating a process in which the examiner selects a small area of distinctive features in one print (a target group), and searches for it in the other print. In order to isolate this key element of fingerprint comparison, we use eye-tracking data to describe the behavior of latent fingerprint examiners on a narrowly defined “find the target” task. Participants were shown a fingerprint image with a target group indicated and asked to find the corresponding area of ridge detail in a second impression of the same finger and state when they found the target location. Target groups were presented on latent and plain exemplar fingerprint images, and as small areas cropped from the plain exemplars, to assess how image quality and the lack of surrounding visual context affected task performance and eye behavior. One hundred and seventeen participants completed a total of 675 trials. Results The presence or absence of context notably affected the areas viewed and time spent in comparison; differences between latent and plain exemplar tasks were much less significant. In virtually all trials, examiners repeatedly looked back and forth between the images, suggesting constraints on the capacity of visual working memory. On most trials where context was provided, examiners looked immediately at the corresponding location: with context, median time to find the corresponding location was less than 0.3 s (second fixation); however, without context, median time was 1.9 s (five fixations). A few trials resulted in errors in which the examiner did not find the correct target location. Basic gaze measures of overt behaviors, such as speed, areas visited, and back-and-forth behavior, were used in conjunction with the known target area to infer the underlying cognitive state of the examiner. Conclusions Visual context has a significant effect on the eye behavior of latent print examiners. Localization errors suggest how errors may occur in real comparisons: examiners sometimes compare an incorrect but similar target group and do not continue to search for a better candidate target group. The analytic methods and predictive models developed here can be used to describe the more complex behavior involved in actual fingerprint comparisons. Electronic supplementary material The online version of this article (10.1186/s41235-019-0160-9) contains supplementary material, which is available to authorized users.

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The impact of fatigue on latent print examinations as revealed by behavioral and eye gaze testing

Cited by 20 publications

References 11 publications

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

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

2018 Impression, Pattern and Trace Evidence Symposium

Gaze behavior and cognitive states during fingerprint target group localization

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