Criminals typically leave behind fingerprints at the crime scene. Forensics helps investigators analyse these fingerprints. However, fingerprints left at a crime scene are often partial, distorted and smudged. Also, if the print is left on something with a confusing background pattern such as a note, it may be difficult to separate the print from the background.
Scientists from the National Institute of Standards and Technology (NIST) and Michigan State University report that they have developed an algorithm that automates a key step in the fingerprint analysis process. Their research was published in IEEE Transactions on Information Forensics and Security this week. The idea was to reduce the human subjectivity to make fingerprint analysis more reliable and more efficient.
The researchers used machine learning to build their algorithm. Unlike traditional programming in which you write out explicit instructions for a computer to follow, in machine learning, you train the computer to recognize patterns by showing it examples. After training was complete, researchers tested the performance of the algorithm by having it score a new series of latent prints. They then submitted those scored prints to the Automated Fingerprint Identification System (AFIS) software connected to a database of over 250,000 rolled prints.
In this test, the researchers knew the correct match for each latent print. The scoring algorithm performed slightly better than the average of the human examiners involved in the study. What made this breakthrough possible, beside recent advances in machine learning and computer vision, was the availability of a large dataset of latent prints. In this case, the Michigan State Police provided the researchers with the testing dataset, after having first stripped the data of all identifying information due to privacy concerns.
The next step for the researchers is to use an even larger dataset. This will allow them to improve the algorithm’s performance and more accurately measure its error rate.
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