An Embedded Fingerprints Classification System based on Weightless Neural Networks

Automatic fingerprint classification provides an important indexing scheme to facilitate efficient matching in large-scale fingerprint databases in Automatic Fingerprint Identification Systems (AFISs). The paper presents a new fast fingerprint classification module implementing on embedded Weightless Neural Network (RAM-based neural network). The proposed WNN architecture uses directional maps to classify fingerprint images in the five NIST classes (Left Loop, Right Loop, Whorl, Arch and Tented Arch) without anyone enhancement phase. Starting from the directional map, the WNN architecture computes the fingerprint classification rate. The proposed architecture is implemented on Celoxica RC2000 board employing a Xilinx Virtex-II 2v6000 FPGA and it is computationally few expensive regards execution time and used hardware resources. To validate the goodness of proposed classificator, three different tests have been executed on two databases: a proprietary and FVC database. The best classification rate obtained is of 85.33% with an execution time of 1.15ms.