NIST releases 3D ballistics research database
do that they need large databases of test-fired bullets and cartridge cases. The databases already in use for solving crimes, such as the National Integrated Ballistics Information Network (NIBIN), are proprietary and contain sensitive information. Researchers cannot download bulk data from them for use in statistical studies.
The NIST database, on the other hand, is open-access and the data is freely available.
Standardized file formats
To seed the database with data, Zheng went to forensics and law enforcement conferences asking agencies to test-fire every 9-mm firearm in their reference collection — 9 mm being the caliber most commonly used in the commission of crimes.
After completing the test fires, labs sent the bullets and cartridge cases to Zheng at NIST, along with data on the gun that fired it. At the lab, technicians scanned these samples using a microscope that produces a high-resolution, 3-D topographic surface map — a virtual model of the physical object itself.
These surface maps produce more detailed comparison data than the two-dimensional images that are traditionally used to match bullets. For this reason, the field of forensic firearms identification is starting to make the transition to 3-D.
NIST notes that to facilitate the transition, NIST co-founded, with microscope manufacturer Cadre Forensics, the Open Forensic Metrology Consortium, or OpenFMC. This group, which includes members from industry, academia, and government, has agreed on a standard file format for 3-D topographic surface maps for use in ballistic imaging. NIST’s new research database will use this open standard, which will allow researchers to easily share data, though the database will also accept traditional two-dimensional images.
The database currently has only about 1,600 test fires — a relatively small number. “But, it’s like the first forensic DNA databases,” Zheng said. “They started off small but filled up quickly.”
Open-source methods
In the meantime, the data that is available is already proving useful. Eric Hare, a Ph.D. student in statistics at Iowa State University, is using the NIST research database to develop bullet-matching algorithms. His work is supported by CSAFE — the Center for Statistics and Applications in Forensic Evidence — which is funded by NIST.
Hare’s algorithms are based on machine learning, which allows a computer to match patterns without being explicitly programmed how to do so. Hare “trains” his algorithms by feeding them pairs of bullets and telling the computer whether they match or not. The computer analyzes the physical features of those bullet pairs and develops a set of statistical rules for predicting whether or not a pair of bullets match.
“Once we’ve trained the algorithm, we give it data without telling it whether it’s a match or a non-match, and we can see how well the algorithm performs,” Hare said.
As the database grows in size the algorithms will become increasingly accurate. And as the database grows in variety to include different types of ammunition and weapons, the algorithms will become more broadly useful. The database already contains test fires from consecutively manufactured firearms, and Hare is testing whether the algorithms can reliably distinguish between them.
Perhaps most importantly, Hare’s code is open source. That means other researchers can check for bias or error in the algorithms, and correct any that are found.
“In high-profile situations where there’s a lot at stake, it would be good if everyone knew exactly what the algorithms were doing,” Hare said.
The FBI recently agreed to contribute a large dataset of test fires from its reference collection of several thousand firearms, which will greatly increase both the size of the database and the diversity of firearms it covers. Zheng hopes that other forensic labs with 3-D microscopes will start uploading their data to the database as well. Because while the database is now available, the real work has just begun.
