BUFFALO, N.Y. -- A shoeprint etched in blood or dust can make a
crucial difference in a criminal case, but it all depends on the
ability of human examiners to identify a matching shoeprint pattern
from thousands in their databases.
It's a laborious, inefficient task.
That's why University at Buffalo computer scientists are
developing tools to make the search-and-match process more like a
Google search and less like hunting for a needle in a haystack.
The research is funded by the U.S. Department of Justice.
"Shoeprint evidence is some of the most widely available type of
evidence at crime scenes," says project leader Sargur Srihari,
Ph.D., SUNY Distinguished Professor in the UB Department of
Computer Science and Engineering.
But like many forensics fields, shoeprint analysis lacks a
"It's a process largely based on human knowledge and intuition,"
Typically, shoeprints left by a suspect will be photographed or
extracted from a surface and submitted to human examiners in
criminal science investigative (C.S.I.) units. Those shoeprint
images are then posted on Internet portals, where they are viewed
by forensic experts around the world. The experts attempt to locate
a match from thousands of shoeprint images in existing
"Analyzing shoeprints consists simply of human experts looking
at shoeprints from a crime scene," says Srihari. "There are no
formal techniques that examiners can use to substantiate a
'positive' finding one way or another. At the same time, bias can
come into play and it's hard to disassociate that kind of knowledge
from evidence you're trying to analyze."
That's where Srihari's research will make a big difference.
"We are developing algorithms for searching and matching
shoeprints," he says.
"We want to automate the process enough so that it works like a
targeted Google search, where the query is the crime scene evidence
and the match will be the list of results that help us determine
which brand of shoe is closest to the print extracted from the
crime scene," Srihari says. "The ultimate goal is to develop a
software package that could narrow down the possibilities for the
examiner to search."
He and his colleagues at UB approached the problem by developing
a large database of their own. They used shoeprints culled from
shopping Web sites such as Zappos and others, which showcase
outsoles of shoes as well as the tops.
They classified the images according to the type of pattern they
exhibited, such as circles, crosses, wavy lines, zig zags.
"We downloaded about 10,000 shoeprint images from commercial Web
sites," Srihari says.
Then the UB researchers created their own version of a crime
scene in their UB lab.
They sprinkled talcum powder on the carpet and invited UB
students, faculty and visitors to walk across the powdered surface,
making impressions that they photographed. The UB scientists then
converted the photographed shoeprints into a digital form that
could be matched with images from the Internet sites.
"The photographed shoeprints are our evidence," says Srihari.
"Our goal is to see how well our algorithms function in matching
the evidence to images in our database."
He expects the UB research to yield quantitative results in
about one year.
Srihari cautioned that while computational methods are likely to
improve the usefulness of shoeprint images in solving crimes,
shoeprints do carry some drawbacks. For example, unlike
fingerprints, which remain essentially the same, shoeprints may
reflect uneven wear over a period of time and shoe treads may
partially wear off, making the computational challenge more
Still, he said, there are substantial advantages to be gained by
better exploitation of shoeprints, since this type of evidence is
quite readily available.
Srihari also has applied his expertise to the forensic use of
handwriting analysis, publishing the first scientific evidence that
handwriting can be proven to be truly individual, given an adequate
He brings to the study of shoeprints a career's worth of
internationally renowned expertise in pattern recognition and the
related field of machine learning. He directs UB's Center of
Excellence in Document Analysis and Recognition (CEDAR), the
world's largest research center devoted to developing new
technologies that can recognize and read handwriting. CEDAR
research includes developing methods for automated analysis of
fingerprints and biometrics.
The University at Buffalo is a premier research-intensive public
university, a flagship institution in the State University of New
York system and its largest and most comprehensive campus. UB's
more than 28,000 students pursue their academic interests through
more than 300 undergraduate, graduate and professional degree
programs. Founded in 1846, the University at Buffalo is a member of
the Association of American Universities.