Automating Detective Work

Every fingerprint is believed to be unique, making it possible to identify an individual by matching a new fingerprint with an image on file, whether to unlock a mobile phone, access a bank account, or solve a murder. Fingerprint examiners, however, do not always agree on whether two print images match and, asked to recheck their work after several months, they sometimes do not even agree with themselves. That is leading to increased use of neural networks, powerhouses for identifying and matching patterns of all sorts, to automate and improve decisions about whether two fingerprints come from the same person. A group of computer scientists decided to use neural networks to test the assumption that no two fingerprints are the same. Using twin neural networks, researchers from Columbia University, Tufts University, and the State University of New York (SUNY) University at Buffalo looked for similarities between different fingerprints in a database from the National Institute of Standards and Technology (NIST).