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Proceedings Paper

Interpretation of fingerprint image quality features extracted by self-organizing maps
Author(s): Ivan Danov; Martin A. Olsen; Christoph Busch
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Paper Abstract

Accurate prediction of fingerprint quality is of significant importance to any fingerprint-based biometric system. Ensuring high quality samples for both probe and reference can substantially improve the system's performance by lowering false non-matches, thus allowing finer adjustment of the decision threshold of the biometric system. Furthermore, the increasing usage of biometrics in mobile contexts demands development of lightweight methods for operational environment. A novel two-tier computationally efficient approach was recently proposed based on modelling block-wise fingerprint image data using Self-Organizing Map (SOM) to extract specific ridge pattern features, which are then used as an input to a Random Forests (RF) classifier trained to predict the quality score of a propagated sample. This paper conducts an investigative comparative analysis on a publicly available dataset for the improvement of the two-tier approach by proposing additionally three feature interpretation methods, based respectively on SOM, Generative Topographic Mapping and RF. The analysis shows that two of the proposed methods produce promising results on the given dataset.

Paper Details

Date Published: 29 May 2014
PDF: 15 pages
Proc. SPIE 9075, Biometric and Surveillance Technology for Human and Activity Identification XI, 907505 (29 May 2014); doi: 10.1117/12.2050676
Show Author Affiliations
Ivan Danov, Technical Univ. of Denmark (Denmark)
Ctr. for Advanced Security Research Darmstadt (Germany)
Martin A. Olsen, Ctr. for Advanced Security Research Darmstadt (Germany)
Christoph Busch, Norwegian Information Security Lab. (Norway)

Published in SPIE Proceedings Vol. 9075:
Biometric and Surveillance Technology for Human and Activity Identification XI
Ioannis A. Kakadiaris; Walter J. Scheirer; Christoph Busch, Editor(s)

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