Computerized System for Aiding the Expert in Evaluating the Degree of Certainty in 2D Shoeprints

In recent years there is a growing demand to fortify the scientific basis of forensic methodology. The 2009 NAS report: "Strengthening Forensic Science in the United States: A Path Forward" focused on the need for a more profound scientific base for forensic science in general, and specifically for pattern comparison. 

The demand for numerical and statistical presentation of the comparison results is seldom fulfilled in the pattern comparison field. Since meaningful databases that can support such calculations do not exist yet, even the best expert’s work is based solely on expertise and experience. Many argue, with much justification, that an expert, even with 20 years of experience, cannot merely rely on his memory when trying to evaluate an examined phenomenon’s weight and frequency of appearance. Such comparison results will not yield consistent numbers. Without a good methodology to compare a feature to many others of its kind, the investigator cannot reliably calculate the criteria to estimate what level of certainty applies in a particular case. Thus any expert’s assertion about the level of certainty in a given case will lack a sufficient numerical and objective base.

This study answers how to evaluate the rareness of acquired accidentals, also known as randomly acquired characteristics (RACs) such as scratches, nicks, tears and holes, as they appear on shoe sole test impressions, and how to address the presence of an array of accidentals on one sole. To achieve this goal, three tools were developed:

1. A large, consistent and adequate database of accidentals. All the accidentals were scanned and their contours digitized.
2. Statistical models to determine chances of finding similar accidentals on other soles in three aspects: shape, location and orientation.
3. Practical tools enabling the shoeprint expert to mark new accidentals and quickly evaluate their evidential value in a more scientifically reliable manner than previously done.

The database was created in the following manner: Test impressions were prepared from worn shoe soles. Accidentals appearing on the sole, such as cuts and scratches, were semi-automatically marked by a qualified examiner using the specially designed "MarkAccidentals" software, developed in this study. Each characteristic was automatically stored in the database, with its digital representation, including its location and calculated orientation. Nearly 9,000 accidentals were recorded and stored.

Since calculating the probability of the accidental's location required many more accidentals, the locations of 20,000 more accidentals were recorded using dedicated software we developed (FaDeMa) that enables rapid location marking. Our database contains test impressions made from shoes of many different designs, and in order to compensate for the effect of different sole designs – the locations were normalized to a standard (universal) sole.

The orientation of each accidental and its accuracy were calculated, and saved in the database.
The statistical algorithm developed, enables the SESA (Statistic Evaluation of Shoeprint Accidentals) system to calculate the probability of finding another feature similar to a particular feature of a scanned and digitized accidental. The system can calculate the chance to get another accidental in the same location, or the same orientation as the examined one. Combining all the probabilities together (by multiplication, assuming independence) reveals the result of the comparison. The number received at the end of the process serves the expert as a guiding number, allowing more objective and accurate results and conclusions.

The system works so far only on test impression, and the research to allow the evaluation of real shoeprint is yet to follow.