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.

References

  1. Bhatnagar J., Kumar A.(2009). On Estimating Some Performance Indices for Biometric Identification. Pattern Recognition, vol. 42( 5), pp. 1805-1818.
  2. De Marsico M., Nappi M., Riccio D. (2012). Entropy in Biometric Face Template Analysis. In: Campilho, Aurélio and Kamel, Mohamed (eds.) Proceedings of International Conference on Image Analysis and Recognition - ICIAR 2012. Lecture Notes in Computer Science, Vol. 7325, pp. 72-79.
  3. De Marsico M., Nappi M., Riccio D., Wechsler H. (2012). Robust Face Recognition for Uncontrolled Pose and Illumination Changes. IEEE Trans. on Systems, Man and Cybernetics, Part A: Systems and Humans, vol.PP, no.99, pp.1-15, doi: 10.1109/TSMCA.2012.2192427
  4. Fowlkes E. B., Mallows C. L. (1983). A Method for Comparing Two Hierarchical Clusterings. J. of the American Statistical Association, 78(383), 553-569.
  5. Kirby M., Sirovich L. (1990). Application of the Karhunen Loeve procedure for the characterization of human faces. IEEE Trans. on Pattern Analysis and Machine Intelligence Vol.12, pp.103-108.
  6. Maltoni D., Maio D., Jain A. K., Prabhakar S. (2003). Handbook of Fingerprint Recognition. Springer.
  7. Phillips P. J., Wechsler H., Huang J., Rauss P.(1998). The FERET Database and Evaluation Procedure for Face Recognition Algorithms, Image and Vision Computing Journal, Vol. 16(5), pp. 295-306.
  8. Rand W. M. (1971). Objective criteria for the evaluation of clustering methods. J. of the American Statistical Association Vol, 66(336), 846-850.
  9. Singh, J. K. (2009). A Clustering and Indexing Technique suitable for Biometric Databases. MSc Thesis, Indian Institute Of Technology Kanpur, Kanpur, India.
  10. Turk M., Pentland A. (1991) Eigen Faces for Recognition. J. of Cognitive Neuroscience, Vol. 3(1), pp. 71-86
  11. Wiskott, L., Fellous, J. M., Krüger, N., von der Malsburg, C. (1996). Face Recognition by Elastic Bunch Graph Matching. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 19(7,) pp.775-779
  12. Zhao H., Yuen P.C. (2008). Incremental linear discriminant analysis for face recognition. IEEE Trans. on Systems, Man and Cybernetics - Part B: Cybernetics. Vol. 38, pp. 210-221
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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