Quantitative evaluation of footwear evidence: Initial workflow for an end-to-end system.

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 with regard to outsole design, size, wear, and randomly acquired characteristics (RACs). We augment this workflow with computer algorithms and statistical analysis so as to improve 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 containing known mated and known nonmated impressions. Our end-to-end workflow facilitates all three of these points and is directly relatable to current practice. We demonstrate the workflow, which includes 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.

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

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. In this paper, we explore the ability of similarity metrics to discriminate between impressions made by a shoe of interest and impressions made by close non-matching shoes. 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.