John Vanderkolk

Behavioral and electrophysiological evidence for configural processing in fingerprint experts

Visual expertise in fingerprint examiners was addressed in one behavioral and one electrophysiological experiment. In an X-AB matching task with fingerprint fragments, experts demonstrated better overall performance, immunity to longer delays, and evidence of configural processing when fragments were presented in noise. Novices were affected by longer delays and showed no evidence of configural processing. In Experiment 2, upright and inverted faces and fingerprints were shown to experts and novices. The N170 EEG component was reliably delayed over the right parietal/temporal regions when faces were inverted, replicating an effect that in the literature has been interpreted as a signature of configural processing. The inverted fingerprints showed a similar delay of the N170 over the right parietal/temporal region, but only in experts, providing converging evidence for configural processing when experts view fingerprints. Together the results of both experiments point to the role configural processing in the development of visual expertise, possibly supported by idiosyncratic relational information among fingerprint features.

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

Eye tracking and behavioral methods were used to assess the effects of fatigue on performance in latent print examiners. Eye gaze was measured both before and after a fatiguing exercise involving fine-grained examination decisions. The eye tracking tasks used similar images, often laterally reversed versions of previously viewed prints, which holds image detail constant while minimizing prior recognition. These methods, as well as a within-subject design with fine grained analyses of the eye gaze data, allow fairly strong conclusions despite a relatively small subject population. Consistent with the effects of fatigue on practitioners in other fields such as radiology, behavioral performance declined with fatigue, and the eye gaze statistics suggested a smaller working memory capacity. Participants also terminated the search/examination process sooner when fatigued. However, fatigue did not produce changes in inter-examiner consistency as measured by the Earth Mover Metric. Implications for practice are discussed.

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

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.

The Policy Implications of Research on Fingerprint Examination Tasks

Most fingerprint comparisons are still done by human examiners, who examine two impressions to determine the amount of perceived detail in agreement. Examiners must rely on their training and experience to determine whether the quality and quantity of detail in agreement is sufficient to warrant an identification decision, which makes their perceptual and decision-making abilities central to our understanding of the strength of fingerprint evidence. Research on latent print examiners has documented the influence of configural processing, greater working memory, and greater consistency of eye gaze among experts relative to novices. All of these lead to universally higher accuracy relative to novices. However, examiners must contend with fatigue and the problem of non-mated prints that are somewhat similar in appearance. Surprisingly, this problem only gets worse as databases increase in size. Currently, the field contends with a relatively high number of erroneous exclusions and inconclusive decisions, which may allow a potentially guilty suspect to remain free from charges. We discuss policy implications that follow directly from the research and suggest future research directions that address unresolved issues.

Discovering correspondences between fingerprints based on the temporal dynamics of eye movements from experts

Latent print examinations involve a process by which a latent print, often recovered from a crime scene, is compared against a known standard or sets of standard prints. Despite advances in automatic fingerprint recognition, latent prints are still examined by human expert primarily due to the poor image quality of latent prints. The aim of the present study is to better understand the perceptual and cognitive processes of fingerprint practices as implicit expertise. Our approach is to collect fine-grained gaze data from fingerprint experts when they conduct a matching task between two prints. We then rely on machine learning techniques to discover meaningful patterns from their eye movement data. As the first steps in this project, we compare gaze patterns from experts with those obtained from novices. Our results show that experts and novices generate similar overall gaze patterns. However, a deeper data analysis using machine translation reveals that experts are able to identify more corresponding areas between two prints within a short period of time.