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Comparing footwear impressions that are close non-matches using correlation-based approaches

NCJ Number
304642
Journal
Journal of Forensic Sciences Volume: 66 Issue: 3 Dated: May 2021 Pages: 890-909
Author(s)
Gautham Venkatasubramanian; Vighnesh Hegde; Sarala Padi; Hari Iyer; Martin Herman
Date Published
May 2021
Length
20 pages
Annotation

This paper explores the ability of similarity metrics to discriminate between impressions made by a shoe of interest and impressions made by close non-matching shoes.

 

Abstract

Forensic activities related to footwear evidence may be broadly classified into the following two categories: (1) intelligence gathering and (2) evidential value assessment. Intelligence gathering provides additional leads for investigators. Assessment of evidential value, as practiced in the United States, involves a trained footwear examiner evaluating the degree of similarity between a known shoe of interest (together with its test impressions) and footwear impressions obtained from a crime scene, by performing side-by-side visual comparisons. However, the need for developing quantitative approaches for expressing similarities during such comparisons is being increasingly recognized by the forensic science community. Close non-matching shoes largely share the same design and size. Therefore, the ability to effectively discriminate between them requires considering, either explicitly or implicitly, not only design and size, but also wear patterns and, to some extent, individual characteristics. This type of discrimination is necessary for assessment of evidential value. The similarity metrics examined in this paper are correlation-based metrics, including normalized cross-correlation, phase-only correlation, AvNCC, and AvPOC. The latter two metrics are based on features obtained from a convolutional neural network. Experiments are performed using Everspry impressions, FBI boot impressions, and the West Virginia University footwear impression collection. The results show that phase-only correlation performs as well as or better than the other metrics in all cases for the datasets we considered. (Publisher Abstract)

Date Published: May 1, 2021