Quantitative Evaluation of Footwear Evidence: Initial Workflow for an End-to-End System

• NCJ Number: 302034
• Journal: Journal of Forensic Science Volume: Online Dated: August 2021
• Author(s): Gautham Venkatasubramanian ; Vighnesh Hegde; Steven P. Lund; Hari Iyer ; Martin Herman
• Date Published: August 2021
• Grant Number(s): DJO-NIJ-17-RO-0202
• Agencies: NIJ-Sponsored
• Award: Quantitative Measures for Footwear Impression Comparisons

Annotation

This article describes an augmentation to the standard workflow for analyzing footwear evidence.

Abstract

In the United States, footwear examiners make decisions about the sources of crime scene shoe impressions using subjective criteria. This has raised questions about the accuracy, repeatability, reproducibility, and scientific validity of footwear examinations. Currently, most footwear examiners follow a workflow that compares a questioned and test impression regarding outsole design, size, wear, and randomly acquired characteristics (RACs). The proposed augmentation of this process uses computer algorithms and statistical analysis to improve the process in the following areas: (1) quantifying the degree of correspondence between the questioned and test impressions with respect to design, size, wear, and RACs; (2) reducing the potential for cognitive bias; and (3) providing an empirical basis for examiner conclusions by developing a reference database of case-relevant pairs of impressions that contain known mated and known non-mated impressions. This end-to-end workflow facilitates all three of these points and is directly relatable to current practice. This workflow was demonstrated, including obtaining and interpreting outsole pattern scores, RAC comparison scores, and final scores on two scenarios—a pristine example (involving very high quality Everspry EverOS scanner impressions) and a mock crime-scene example that more closely resembles real casework. These examples not only demonstrate the workflow but also help identify the algorithmic, computational, and statistical challenges involved in improving the system for eventual deployment in casework. (publisher abstract modified)