Neurotechnology's palm algorithm took lead at FVC
18 February 2019 16:48 GMT

Neurotechnology, a provider of deep learning-based solutions, robotics and high-precision biometric identification technologies, has reported the latest FVC-onGoing test results for their Palm Print recognition algorithm.

The Palm Print Matcher, part of Neurotechnology's MegaMatcher SDK, was ranked as the most accurate for both full and partial palm prints, as the fastest partial palm print matcher and the fastest full-print matcher out of the five most accurate matchers. Neurotechnology's algorithm also has the smallest template size overall, both in full palm print and partial (lower) palm print datasets.

"Our expertise in fingerprint recognition technologies carries over to palm print matching," said Dr. Justas Kranauskas, head of the biometric research department for Neurotechnology. "Though the palm print is a larger, more detailed recognition task, our experience in this field allows us to bring the most accurate, highest efficiency application available to the palm print recognition market."

Because of its complexity, palm print template matching requires much more computational time than single or multiple fingerprint matching. Focusing on speed, as well as accuracy, Neurotechnology has developed a palm print matching algorithm that is the fastest partial (lower) palm print matcher and fastest full palm print matcher out of the top five most accurate full palm print matchers in FVC-onGoing. It is suitable for both 1-to-1 (verification) and 1-to-many (identification) applications.

Neurotechnology's palm print matching engine is included in MegaMatcher Standard and Extended SDKs.