Entropy based Biometric Template Clustering

Michele Nappi, Daniel Riccio, Maria De Marsico

2013

Abstract

Though speed and accuracy are two competing requirements for large scale biometric recognition, they both suffer from large database size. Clustering seems promising to reduce the search space. This can improve accuracy, but may even contrarily affect it by a poor selection of the candidate cluster for the search. We present a novel technique that exploits gallery entropy for clustering. The comparison with K-Means demonstrates that we achieve a better clustering result, yet without fixing the number of clusters a-priori.

Paper Citation

in Harvard Style

Nappi M., Riccio D. and De Marsico M. (2013). Entropy based Biometric Template Clustering . In Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-8565-41-9, pages 560-563. DOI: 10.5220/0004266205600563

in Bibtex Style

@conference{icpram13,
author={Michele Nappi and Daniel Riccio and Maria De Marsico},
title={Entropy based Biometric Template Clustering},
booktitle={Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2013},
pages={560-563},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004266205600563},
isbn={978-989-8565-41-9},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Entropy based Biometric Template Clustering
SN - 978-989-8565-41-9
AU - Nappi M.
AU - Riccio D.
AU - De Marsico M.
PY - 2013
SP - 560
EP - 563
DO - 10.5220/0004266205600563