Fit-sphere unwrapping and performance analysis of 3D fingerprints

Yongchang Wang, Daniel Lau, and Laurence G. Hassebrook
Abstract
To solve problems associated with conventional 2D fingerprint acquisition processes including skin deformations and print smearing, we developed a noncontact 3D fingerprint scanner employing structured light illumination that, in order to be backwards compatible with existing 2D fingerprint recognition systems, requires a method of unwrapping the 3D scans into 2D equivalent prints. For the latter purpose of virtually flattening a 3D print, this paper introduces a fit-sphere unwrapping algorithm. Taking advantage of detailed 3D information, the proposed method defuses the unwrapping distortion by controlling the distances between neighboring points. Experimental results will demonstrate the high quality and recognition performance of the 3D unwrapped prints versus traditionally collected 2D prints. Furthermore, by classifying the 3D database into high- and low-quality data sets, we demonstrate that the relationship between quality and recognition performance holding for conventional 2D prints is achieved for 3D unwrapped fingerprints. [Full Text]





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