The role and duties of the shoeprint/toolmark examiner in forensic laboratories

Liukkonen, Markku; Majamaa, Heikki; Virtanen, Johanna

doi:10.1016/0379-0738(96)01971-8

Cited by 6 publications

(2 citation statements)

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“…This architecture consists of three branches-an encoder F θ (·) that takes as input a shoeprint image X, a delta branch G ω (·) that encodes a representation of time from a parameter ∆ t , and a decoder H β (·) that learns an upsampling function to predict the wear pattern. These branches take the form shown in (1).…”

Section: Methodsmentioning

confidence: 99%

“…Among the many forms of physical evidence found at crime scenes, shoeprints are one of the most frequently seen, with a high degree of evidential value attributed to them. Marks and prints formed by the footwear worn by the criminal(s) are frequently found at scenes-of-crime and their study was being recorded as early as 1786 [1]. This is in part due to the ability of a shoeprint to uniquely identify an individual, by evaluation of the combination of tears, nicks, cuts, scratches and other abrasions that form on the outsole as a function of wear.…”

Section: Introductionmentioning

confidence: 99%

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Learning Wear Patterns on Footwear Outsoles Using Convolutional Neural Networks

Francis

Sharifzadeh

Newton

et al. 2019

2019 18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/13th IEEE International

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Footwear outsoles acquire characteristics unique to the individual wearing them over time. Forensic scientists largely rely on their skills and knowledge-gained through years of experience-to analyse such characteristics on a shoeprint. In this work, we present a deep learning model that, for the first time, can predict the wear pattern on a unique dataset of shoeprints that captures the life and wear of a pair of shoes. We also present an additional architecture able to reconstruct the outsole back to its original state on a given week, and provide empirical evaluations of the performance of both models.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%