NexID Upgrades Fake Finger Detection Solution
LONDON – Oct. 21, 2014 – NexID Biometrics LLC, whose spoof-mitigation technology boosts protection in applications with critical security-authentication needs, today announced a major improvement in the performance of its industry leading fake-finger-detection solution.
The company, based in Potsdam, N.Y., in the United States, announced the new upgrade to its fake-finger-detection SDK at the three-day Biometrics 2014 conference, which began here today at the Queen Elizabeth II Conference Centre in Westminster.
NexID said version 1.2 of its SDK, targeted for initial deployments in November 2014, boosts the accuracy rate range to 96.5 to 99.5 percent in catching bogus identification attempts across a variety of fingerprint-sensor types, such as touch or swipe, and technologies, including optical and capacitance.
The increased performance was achieved without a significant impact on computing time or resources, thereby maintaining high levels of user convenience, the company said,
“Our ongoing research has yielded additional fingerprint-imaging features that are highly effective in differentiating images captured from authentic versus fake fingerprints,” said NexID Chief Operating Officer Mark Cornett. “In essence, we have expanded our portfolio of fingerprint-imaging features and that translates into higher accuracy.”
He said new SDK’s equal error rates – the inverse of accuracy in correctly identifying a spoof image or not — have been reduced by half from the prior version to 0.5 to 3.5 percent.
“We continue to explore ways to reduce equal error rates toward zero so that customers who deploy fingerprint-identification sensors can have complete confidence that authentications of the people using the sensors are accurate,” Cornett said. “We’re not aware of any software-based, fingerprint spoofing-mitigation technology today that’s more rigorous than NexID’s latest software improvement.”
Last month, NexID announced a complete overhaul of its software capabilities resulting in a breakthrough that makes its solution applicable to fingerprint sensors in both mobile devices and embedded modules, such as ATMs and terminals for entry access, time and attendance, and point of sale.
The company said then it had redesigned its software for integration with embedded “match-on-chip” implementations where matching and fake-finger-detection software execute on the same processor that controls the fingerprint sensor’s image-capture, enrollment and authentication operations.
Taken together, the dramatic improvement in accuracy achieved with version 1.2 of the SDK and enhancements enabled by the software redesign mean state-of-art fake finger detection is now available across the entire spectrum of fingerprint sensor devices.