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An undergraduate-led team at Columbia Engineering used AI to challenge the forensic assumption that fingerprints from different fingers of the same person are unmatchable, discovering a new kind of forensic marker. Despite initial skepticism, their findings, which could revolutionize forensic science, were published in Science Advances. Credit: SciTechDaily.com
Columbia engineers have built a new AI that shatters a long-held belief in forensics–that fingerprints from different fingers of the same person are unique. It turns out they are similar, only we’ve been comparing fingerprints the wrong way!
From “Law and Order” to “CSI,” not to mention real life, investigators have used fingerprints as the gold standard for linking criminals to a crime. But if a perpetrator leaves prints from different fingers in two different crime scenes, these scenes are very difficult to link, and the trace can go cold.
It’s a well-accepted fact in the forensics community that fingerprints of different fingers of the same person — ”intra-person fingerprints” — are unique, and therefore unmatchable.
Research led by Columbia Engineering undergraduate
A team led by Columbia Engineering undergraduate senior Gabe Guo challenged this widely held presumption. Guo, who had no prior knowledge of forensics, found a public U.S. government database of some 60,000 fingerprints and fed them in pairs into an artificial intelligence-based system known as a deep contrastive network. Sometimes the pairs belonged to the same person (but different fingers), and sometimes they belonged to different people.
![Saliency Map Fingerprint](https://scitechdaily.com/images/Saliency-Map-Fingerprint-777x583.jpg)
Saliency map highlights areas that contribute to the similarity between the two fingerprints from the same person. Credit: Gabe Guo,/Columbia Engineering
AI has potential to greatly improve forensic accuracy
Over time, the AI system, which the team designed by modifying a state-of-the-art framework, got better at telling when seemingly unique fingerprints belonged to the same person and when they didn’t. The
” data-gt-translate-attributes=”[{“attribute”:”data-cmtooltip”, “format”:”html”}]” tabindex=”0″ role=”link”>accuracy for a single pair reached 77%. When multiple pairs were presented, the accuracy shot significantly higher, potentially increasing current forensic efficiency by more than tenfold. The project, a collaboration between Hod Lipson’s Creative Machines lab at Columbia Engineering and Wenyao Xu’s Embedded Sensors and Computing lab at
A need for broader datasets
The team is aware of potential biases in the data. The authors present evidence that indicates that the AI performs similarly across genders and races, where samples were available. However, they note, more careful validation needs to be done using datasets with broader coverage if this technique is to be used in practice.
Transformative potential of AI in a well-established field
This discovery is an example of more surprising things to come from AI, notes Lipson. “Many people think that AI cannot really make new discoveries–that it just regurgitates knowledge,” he said. “But this research is an example of how even a fairly simple AI, given a fairly plain dataset that the research community has had lying around for years, can provide insights that have eluded experts for decades.”
He added, “Even more exciting is the fact that an undergraduate student, with no background in forensics whatsoever, can use AI to successfully challenge a widely held belief of an entire field. We are about to experience an explosion of AI-led scientific discovery by non-experts, and the expert community, including academia, needs to get ready.”
Reference: “Unveiling intra-person fingerprint similarity via deep contrastive learning” by Gabe Guo, Aniv Ray, Miles Izydorczak, Judah Goldfeder, Hod Lipson and Wenyao Xu, 12 January 2024, Science Advances.
DOI: 10.1126/sciadv.adi0329