Abstract
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.
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Yu, C., Busey, T., Vanderkolk, J. (2011). Discovering Correspondences between Fingerprints Based on the Temporal Dynamics of Eye Movements from Experts. In: Sako, H., Franke, K.Y., Saitoh, S. (eds) Computational Forensics. IWCF 2010. Lecture Notes in Computer Science, vol 6540. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19376-7_14
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DOI: https://doi.org/10.1007/978-3-642-19376-7_14
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