Matthew T. Mangino
CREATORS
December 30, 2025
Fingerprints
have long been considered the gold standard of crime investigation techniques.
As early as 1903, America — with its new young president and former New York
City police commissioner Teddy Roosevelt — began using fingerprints in criminal
investigations. Fingerprint analysis became a "thing" back in the
mid-18th century in India.
Within a
couple of decades, the FBI began cataloging fingerprints. Today, the bureau is
storing more than 200 million fingerprints.
Until
recently, the FBI described fingerprint identification as 100% infallible. That
is no longer the case. In the last twenty years, there hasn't been a lot of
good news when it comes to forensic analysis, including fingerprint analysis.
What do we
know about fingerprints? Impressions of fingerprints are left behind on various
surfaces by the natural secretions of sweat. The friction ridges, the raised
portion of the epidermis on fingers consisting of one or more connected ridges,
are often the point of comparison.
First, an
intentional recording of the fingerprint is made with black ink on a white card
or recorded digitally. These are often collected after arrest and secured in a
database. At a crime scene a "latent print," the chance recording of
a fingerprint deposited on a surface, is captured through chemical methods and
brought into a lab for expert analysis.
Fingerprint
identification came under scrutiny in 2004. The FBI publicly acknowledged the
fingerprint misidentification of an Oregon lawyer wrongfully implicated in a
terrorist bombing in Madrid — a place he had never visited.
Through a
study conducted in 2004, cognitive neuroscientist Itiel Dror found that
otherwise competent and well-meaning experts were swayed by what they knew
about a case submitted for analysis. Dror's study demonstrated that if an
analyst knew that the suspect confessed or was arrested, the analyst's findings
could be influenced. According to Frontline, cognitive bias seeped into the
process even with the best-trained experts.
In steps
deep learning, the use of multi-layered artificial intelligence to
automatically learn complex patterns from vast amounts of data.
A recent
study published in Science Advances entitled "Unveiling intra-person
fingerprint similarity via deep contrastive learning" revealed a
breakthrough in fingerprint analysis.
Law
enforcement agencies worldwide have operated under the long-standing belief
that no two fingerprints are alike, even across the ten fingers of a single
individual.
The
authors suggest that an investigator can sidestep the same-finger limitation by
exploiting nontraditional fingerprint features. "Past studies provided
evidence that fingerprint patterns may be partially genetically determined
which implies that there could be similarities among fingerprints from the same
person," the authors found.
In
addition, "recent research shows that partial fingerprints from different
users have common features that can be exploited to fool authentication
systems."
The study
concluded, "the ability to process and match distinct fingerprint samples
from the same individual opens new investigative possibilities, particularly in
cases where fingerprints are partial or collected under suboptimal
conditions."
This
breakthrough moves investigators away from matching the best print with the
exact finger of a suspect. The study found, "The new AI model reduces this
dependency by identifying shared features that remain stable across different
fingers."
How does
fingerprint evidence get in front of a jury?
Specialized
rules of evidence allow expert testimony if the conclusions are based on
knowledge, skill, experience, training or education in the techniques involved
and the specialized knowledge will assist the judge or jury to understand the
evidence or to determine a fact in issue. The testimony must be based on
reliable principles and methods, consistently applied.
Here is
the new dilemma. If Artificial Intelligence is used to determine a fingerprint
match, how does the expert witness convey the process of using AI to evaluate
the evidence? This information is crucial to whether a judge allows the
expert's opinion and whether the opinion helps jurors understand the
reliability of evidence.
Matthew T.
Mangino is of counsel with Luxenberg, Garbett, Kelly & George P.C. His book
The Executioner's Toll, 2010, was released by McFarland Publishing. You can
reach him at www.mattmangino.com and follow him on Twitter @MatthewTMangino
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