UB Research Revolutionizes Fingerprint Analysis
Crime scene forensic analysis has long functioned on the premise that a person's unique identity is hidden in the tiny loops and swirls of their fingerprints, but teasing that information out of the incomplete prints left at crime scenes is still an inexact science, at best.
Now, a University at Buffalo professor -- who in 2001 provided the first scientific evidence that fingerprints truly are unique – has developed a way to computationally determine the rarity of a particular fingerprint and, thus, how likely it is to belong to a particular crime suspect.
The paper, "Evaluation of Rarity of Fingerprints in Forensics," will be presented by Sargur N. Srihari, PhD, co-author and SUNY Distinguished Professor in the UB Department of Computer Science and Engineering, at the Proceedings of Neural Information Processing Systems conference today in Vancouver.
By combining machine learning with the ability to automate the extraction of specific patterns or features in a fingerprint and then comparing it with large databases of random fingerprints, Srihari and co-researchers are able to come up with a probability that a specific fingerprint would randomly match another in a database of a given size.
The UB research is the first attempt to determine the rarity of a fingerprint using computational tools.