We use these features to train a Random Forest to help differentiate between a match and a non-match. Using the forest, we predict on the features you just extracted. Your predicted probability of a match is given below.

Here are the values of the features computed on the aligned bullet signatures.

This app will walk through the steps used to programmatically determine the probability that two bullets were fired from the same gun barrel. We compare at the bullet land level.

This work was developed in collaboration with Omni Analytics Innovative Technologies Initiaitve (OAITI) and the Center for Statistics and Applications in Forensic Evidence (CSAFE) at Iowa State University. These procedures are fully open-source and transparent. For more details on the underlying code, please see the GitHub repository for the companion R package

Hare, E., Hofmann, H., and Carriquiry, A.,

Hare, E., Hofmann, H., and Carriquiry, A.,

Below you will find surface topologies of the two bullet lands you have uploaded. You can rotate, pan, zoom, and perform a number of other functions to examine the surfaces.

Our goal is to find a

We step through cross-sections of each land at a fixed step size, and uses the CCF (cross-correlation function) to determine stability (a high CCF means that subsequent cross-sections are similar to each other). We begin this procedure near the area where striation markings are typically most pronounced.

The cross-sections you have taken are shown below. Our next goal will be to remove the grooves, which contain no relevant information for matching, and greatly exceed the size of a typical striation mark.

We use a double-pass smoothing method to determine the location of the grooves.

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We have removed the grooves, but the global structure of the cross-section dominates the overall appearance, making striae more difficult to locate.

We are going to fit a loess regression to model this structure. The loess regression includes a span parameter which adjusts the amount of smoothing used. Different values will yield different output. We default to a span of 0.03, but this may be adjusted as desired.

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The residuals from the loess fit we have extracted in the previous stage are called the bullet

Because the signatures are defined by the residuals, the peaks and valleys visible in this plot represent the striation markings we are looking for. In order to make matching easier, our next step is to align the two signatures. We suggest an optimal alignment, but it can be adjusted if necessary.

With aligned signatures, we now turn our attention to determining what constitutes a peak or a valley. Since there is a lot of noise, this step involves one more smoothing pass.

We can specify a smoothing window, called the

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