After a crime is committed, investigators must reconcile the evidence found at the scene with a narrative of the crime. For instance, shoeprints at the scene might be linked to shoes in the suspect's possession, which would suggest the suspect's shoes were at the scene. During this process, the shoes are examined and the two prints are compared. In court, the prosecution must then describe the value of that evidence - how much information should it provide to the jury concerning the suspect's guilt or innocence?

Part of the calculation of that information is to determine what the probability of a coincidental match is, that is, what's the probability that some random individual would also have a shoe with a tread pattern similar to the print at the crime scene? If that probability is high, the evidence is less valuable, but if it's low, then the jury should treat the evidence with much more weight.

Probability would tell us that this is a fairly simple calculation. We first define the comparison population, that is, the population of people who could have made the print - say, individuals in Lincoln.
Then, we would sample from that comparison population to see what shoes the people in the comparison set have. We would then identify similar shoes - shoes which could have made the print at the crime scene, and estimate the probability of a coincidental match as the number of similar shoes divided by the size of the comparison population sample.

This problem has been called "unsolveable" for good reasons - it wasn't tractable with the technology available at the time. But occasionally, you can imagine a solution that depends on a certain technology being invented... and that's where we are today. So why didn't it seem possible?

For starters, while there are databases for other pattern match evidence, like tire tread patterns, there is not a complete database of all shoes sold in the US. Tires have to be certified; shoes do not. There are also many more manufacturers for shoes, new models are released all the time. A single model may have multiple tread patterns, a single tread pattern may be used on multiple shoe models. The tread pattern may change depending on the style of shoe; there are also different molds for a single size/tread combination, and these molds may have different characteristics.

You may think about instead tracking sales data - surely, we could get a database of shoe preferences that way? How many of you have shoes in your closet that you've never worn? That you've worn once? Or less than once a year? Purchase data doesn't provide a realistic picture of the shoes people wear day to day - most of us have one or two "favorites". In addition, that provides us no information about how the match probability changes with season and weather. Obviously, most people aren't wearing sandals in the middle of winter, but there aren't any studies of footwear frequency to back that up with data.

In addition, we know that local populations differ wildly in footwear choices. The footwear worn on campus might not be all that similar to the footwear worn near the capitol building, because the populations that frequent them are different and the dress codes are different. This is another problem with sales data - it doesn't generalize well to the hyper-local regions that we might want to consider when characterizing coincidental match probability.

So how do we solve this problem? How do we collect this data at a (potentially) neighborhood level?

It's cool to be able to get the data out of the scanner, but we know that examiners don't have time to mark up all of those images manually, so we're working on being able to generate images like this automatically.

Note: This image is a simulation of what we're hoping to get out of a picture from the scanner. We're slowly making progress on this, but there are also other database systems like those used in the UK for cataloguing shoe features automatically.

We're hoping to get some idea of the external shoe sole size (calculated using the known distance from the surface of the scanner to the camera), as well as logo recognition where such information is visible, and feature identification maps of the tread pattern. Obviously, this will depend both on the image quality/lighting and the wear of the shoes, but it is something we want to make available and easy to use for examiners, ideally hosting the identification software so that you can access it externally using a web browser.

We're hoping to give a webinar talking about this in about a year's time, so for now you're going to be left with this teaser image. Keep an eye on CSAFE's webinar schedule, though!