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Notre Dame researchers develop iris recognition software using new method


Researchers at the University of Notre Dame recently released software that helps determine similarity between iris scans using a new method in an effort to improve the iris recognition technology used in forensics and law enforcement, according to a report by The Observer.

Released last month on Notre Dame’s Office of Technology Transfer, the software was developed by computer science and engineering graduate student Jianxu Chen, Duda Family professor of engineering Patrick Flynn, computer science and engineering professor Danny Chen, and former computer science and engineering graduate student Feng Shen.

“Iris recognition is very accurate; [current methods] can reach 99.99 percent accuracy,” Jianxu Chen said. “However, that is based on some ‘black box’ method. People without expert knowledge on image processing can not understand what is going on there. … We wanted to make it visualizable and interpretable by humans.”

Despite iris recognition’s accuracy, fingerprint recognition is a more common biometric method used in law enforcement and forensics.

“When you do fingerprint matching, you can just say, ‘There’s a loop here; there’s a circle there’, and you just match them — there’s a very apparent pattern there,” Jianxu Chen said. “We want to mimic this process. Namely, we detect some features on the iris images, so whenever people want to match them, they just find whether you have this feature or that feature … If you see five or six matched features, it’s probably a good sign that the irises are from the same person.”

Jianxu Chen developed a more accurate algorithm for the software, which Shen had suggested and studied initially.

Although the new software is not yet as accurate as more common methods, he said it is important that the results of iris recognition tests are able to be read by humans.

“Their accuracy may be around 99.99 percent — ours is maybe 97 to 98 percent. However, the benefit is that we want humans to judge the correctness. We can bring our result to humans,” Jianxu Chen said. “If you give this picture to an FBI officer, it will mean nothing to them. However, if you bring [the results of the new recognition software] to them, after some training, they can do this matching reliably.”

The researchers have made their software open source so that any potential collaborators have the opportunity to improve the accuracy of the software’s readings, and ultimately accelerate it for real-world applications in law enforcement, criminal justice and forensics.

“This method is not that accurate at the current stage,” he said. “We want to push it further. By myself, it would be very hard. I want to take suggestions from others. When 10 or 20 other people are studying this problem, maybe they can provide a better answer and push it further.”

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