1. bookVolume 67 (2016): Issue 1 (September 2016)
Journal Details
License
Format
Journal
eISSN
1338-9750
First Published
12 Nov 2012
Publication timeframe
3 times per year
Languages
English
access type Open Access

Fingerprint Recognition System Using Artificial Neural Network as Feature Extractor: Design and Performance Evaluation

Published Online: 25 Feb 2017
Volume & Issue: Volume 67 (2016) - Issue 1 (September 2016)
Page range: 117 - 134
Received: 01 Dec 2016
Journal Details
License
Format
Journal
eISSN
1338-9750
First Published
12 Nov 2012
Publication timeframe
3 times per year
Languages
English
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 results. 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 demonstrated 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 networks into fingerprint recognition process, implemented and documented our own software solution for fingerprint identification based on neural networks whose impact 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 sensor, image enhancement module responsible for image quality restoration, Level-1 and Level-2 feature extraction module based on neural network, and finally fingerprint matching module using the industry standard BOZORTH3 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 conclusions about a very positive impact of neural networks on overall recognition rate, specifically in low quality.

Keywords

MSC 2010

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