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“Do you think every fingerprint is really unique?”
It’s a question a professor asked Gabe Guo during a casual chat while he was stuck at home during the Covid-19 shutdowns, waiting to start his freshman year at Columbia University. “Little did I know that conversation would set the stage for the focus of my life for the next three years,” Guo said.
Guo, now a senior in Columbia’s computer science department, led a team that conducted a study on the topic, with Professor Wenyao Xu of the University at Buffalo as one of his co-authors. Published this week in the journal Science Advances, the paper apparently contradicts a long-accepted truth about fingerprints: Guo and his colleagues argue that they are not all unique.
In fact, journals rejected the paper several times before the team appealed and finally got it accepted in Science Advances. “At first there was a lot of opposition from the forensic community,” recalled Guo, who had no experience in forensic science before the study.
“During the first or second version of our article, they said that it is a well-known fact that no two fingerprints are the same. I guess that really helped improve our study, because we kept adding more data (increasing precision) until finally the evidence was incontrovertible,” he said.
A new look at old prints.
To arrive at their surprising results, the team employed an artificial intelligence model called a deep contrastive network, which is commonly used for tasks such as facial recognition. The researchers added their own touch to it and then fed it a US government database of 60,000 fingerprints in pairs that sometimes belonged to the same person (but from different fingers) and other times belonged to different people. .
While working, the AI-based system discovered that fingerprints from different fingers of the same person shared strong similarities and could therefore tell when the fingerprints belonged to the same individual and when they did not, with accuracy for a single pair. reaching a maximum of 77%, which apparently disproves that each fingerprint is “unique.”
“We found a rigorous explanation for why this is so: the angles and curvatures in the center of the fingerprint,” Guo said.
For hundreds of years of forensic analysis, he added, people have been looking at different features called “minutiae,” the branches and endpoints on the ridges of fingerprints that are used as traditional markers for fingerprint identification. “They are great for comparing fingerprints, but they are not reliable for finding correlations between fingerprints from the same person,” Guo said. “And that’s the idea we had.”
The authors said they are aware of potential biases in the data. Although they believe the AI system works very similarly across genders and races, for the system to be usable in real forensics, more careful validation is required by analyzing a larger fingerprint database and wide, according to the study.
However, Guo said he is confident the discovery can improve criminal investigations:
“The most immediate application is that it can help generate new leads for unsolved cases, where the fingerprints left at the crime scene are from different fingers than those on file,” he said. “But on the other hand, this will not only help catch more criminals. In reality, this will also help innocent people who may no longer have to be investigated unnecessarily. And I think that is a victory for society.”
‘A storm in a teacup’?
Using deep learning techniques on fingerprint images is an interesting topic, according to Christophe Champod, a professor of forensic science at the School of Criminal Justice at the University of Lausanne in Switzerland. However, Champod, who was not involved in the study, said he doesn’t believe the work uncovered anything new.
“Their argument that these shapes are somehow correlated between fingers has been known since the beginning of fingerprinting, when it was done manually, and has been documented for years,” he said. “I think they have oversold their newspaper, in my opinion due to lack of knowledge. “I’m happy they’ve rediscovered something known, but essentially it’s a tempest in a teacup.”
In response, Guo said that no one had quantified or systematically used similarities between fingerprints from different fingers of the same person to the degree that the new study does.
“We are the first to explicitly point out that the similarity is due to the orientation of the ridge in the center of the fingerprint,” Guo said. “In addition, we are the first to try to compare fingerprints from different fingers of the same person, at least with an automated system.”
Simon Cole, a professor in the department of criminology, law and society at the University of California, Irvine, agreed that the article is interesting but said its practical usefulness is overstated. Cole was also not involved in the study.
“We weren’t ‘wrong’ about the fingerprints,” he said of the forensic experts. “The unproven but intuitively true claim that no two fingerprints are ‘exactly the same’ is not refuted by the discovery that fingerprints are similar. “It has always been known that the fingerprints of different people, as well as those of the same person, are similar.”
The newspaper said the system could be useful at crime scenes where fingerprints found are from fingers other than those shown in the police record, but Cole said this can only happen in rare cases, because when fingerprints are taken fingerprints, the 10 fingers and often The palms are routinely recorded. “It’s not clear to me when they believe authorities will have on file only some, but not all, of an individual’s fingerprints,” she said.
The team behind the study says it is confident in the results and has opened up the AI code for others to verify, a decision that both Champod and Cole praised. But Guo said the importance of the study goes beyond fingerprints.
“It’s not just about forensic science, it’s about artificial intelligence. Humans have been looking at fingerprints since we existed, but no one noticed this similarity until our AI analyzed them. That simply speaks to the power of AI to automatically recognize and extract relevant features,” he said.
“I think this study is just the first domino in a huge sequence of these things. “We will see people using AI to discover things that were literally hiding in plain sight, right in front of our eyes, like our fingers.”
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