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NIST advancing contactless fingerprint biometric technology with test fingers

NIST advancing contactless fingerprint biometric technology with test fingers

To improve the accuracy of the emerging contactless fingerprint reader technology, researchers with the U.S. National Institute of Standards and Technology (NIST) have developed a new way of comparing the images captured through contact versus contactless processes — by creating standardized test fingers.

While traditional fingerprint devices capture 2D images, contactless fingerprint readers are designed to capture the three-dimensional characteristics of fingerprints, including ridges and valleys. To test the accuracy of this new technology, NIST researchers have created standard reference test fingers that can be used to compare 3D and 2D images. These fingers are made with flexible 3D-printed bones, encased in a clear material, and engraved with a fingerprint on the surface.

The reasoning behind the creation of the test fingers is explained a NIST Special Publication from a year ago.

By incorporating simulated bones, the reference test finger can better simulate the 3D nature of a human finger. This allows for more accurate testing of contactless fingerprint readers, ensuring they can effectively capture and recognize the unique features of a fingerprint.

As part of the certification process, the NIST Fingerprint Registration and Comparison Tool (NFRaCT) aids in the evaluation of contactless fingerprint acquisition devices and their interoperability with legacy devices. This software tool allows interested parties seeking certification to gather fingerprint images and performance data without exchanging any underlying images, ensuring privacy and security throughout the certification process.

These testing fingers provide a more human-like form factor to help evaluate the accuracy and performance of contactless fingerprint readers and provide a consistent benchmark for comparison with 2D device captures. They also help to simplify the certification process, check the consistency of fingerprint images between databases, and help developers verify that their contactless fingerprint readers meet industry standards.

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