This development will not only help today’s fingerprint-matching technology do its job better, but could eventually lead to improvements in security.
What Jain, a University Distinguished Professor of computer science and engineering, and his team did was develop a method that takes a two-dimensional image of a fingerprint and maps it to a 3-D finger surface.
The 3-D finger surface, complete with all the ridges and valleys that make up the human fingerprint, is made using a 3-D printer. It creates what Jain’s team called a fingerprint “phantom.”
Imaging phantoms are common in the world of medical imaging. For example, to make sure an MRI machine or a CT scanner is working properly, it needs to first image an object of known dimensions and material properties.
“In health care, a 3-D heart or kidney can be created,” Jain said. “Because the dimensions are known, they can be put into a scanner and the imaging system can be calibrated.”
In this case, the ultimate goal is to have a precise fingerprint model with known properties and features that can be used to calibrate existing technology used to match fingerprints.
“When I have this 3-D fingerprint phantom, I know its precise measurements,” said Jain. “And because I know the true dimensions of the fingerprint features on this phantom, I can better uate fingerprint readers.”
While the 3-D model doesn’t yet have the exact texture or feel of a real finger, it could advance fingerprint sensing and matching technology.
“Tools like this would help improve the overall accuracy of fingerprint-matching systems, which eventually leads to better security in applications ranging from law enforcement to mobile phone unlock,” Jain said.
Jain, who has a B.Tech degree from IIT Kanpur and MS and PhD degrees from Ohio State University, has six US patents on fingerprint matching and has written a number of books on biometrics and fingerprint/facial recognition.
Additionally, Jain has also received a number of prestigious awards for contributions to pattern recognition and biometrics.