Fingerprint recognition system using artificial neural network as feature extractor: design and performance evaluation

Abstract

Performance of modern automated fingerprint recognition systems
is heavily influenced by accuracy of their feature extraction algorithm. Nowadays,
there are more approaches to fingerprint feature extraction with acceptable re-
sults. Problems start to arise in low quality conditions where majority of the
traditional methods based on analyzing texture of fingerprint cannot tackle this
problem so effectively as artificial neural networks. Many papers have demon-
strated uses of neural networks in fingerprint recognition, but there is a little
work on using them as Level-2 feature extractors. Our goal was to contribute to
this field and develop a novel algorithm employing neural networks as extractors
of discriminative Level-2 features commonly used to match fingerprints.
In this work, we investigated possibilities of incorporating artificial neural net-
works into fingerprint recognition process, implemented and documented our own
software solution for fingerprint identification based on neural networks whose im-
pact on feature extraction accuracy and overall recognition rate was evaluated.
The result of this research is a fully functional software system for fingerprint
recognition that consists of fingerprint sensing module using high resolution sen-
sor, image enhancement module responsible for image quality restoration, Level-1
and Level-2 feature extraction module based on neural network, and finally fin-
gerprint matching module using the industry standard BOZORTH-3 matching
algorithm. For purposes of evaluation we used more fingerprint databases with
varying image quality, and the performance of our system was evaluated using
FMR/FNMR and ROC indicators. From the obtained results, we may draw con-
clusions about a very positive impact of neural network on overall recognition
rate, specifically in low quality.