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A multi-matcher system based on knuckle-based features

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Abstract

We describe a new multi-matcher biometric approach, using knuckle-based features extracted from the middle finger and from the ring finger, with fusion applied at the matching-score level. The features extraction is performed by Radon transform and by Haar wavelet, then these features are transformed by non-linear Fisher transform. Finally, the matching process is based on Parzen window classifiers. Moreover, we study a method based on tokenised pseudo-random numbers and user specific knuckle features. The experimental results show the effectiveness of the system in terms of equal error rate (EER) (near zero equal error rate).

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Notes

  1. Implemented as in the function adapthisteq.m of matlab 7.0 image processing toolbox.

  2. The matlab code is available at http://bias.csr.unibo.it/nanni/diffusion.rar.

  3. window width parameter = 1.

  4. Implemented as in the function adapthisteq.m and imadjust.m of matlab 7.0 image processing toolbox.

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Acknowledgment

This work has been supported by European Commission IST-2002-507634 Biosecure NoE projects. Several methods (PCA, P, N, F and NL) have been implemented as in PRTools 3.1.7 (http://130.161.42.18/prtools/). The authors would like to thank Federico D’Almeida for sharing the diffusion filtering toolbox.

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Authors and Affiliations

  1. DEIS, IEIIT-CNR, Università di Bologna, Viale Risorgimento 2, 40136, Bologna, Italy

    Loris Nanni & Alessandra Lumini

Authors
  1. Loris Nanni
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  2. Alessandra Lumini
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Correspondence to Loris Nanni.

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Nanni, L., Lumini, A. A multi-matcher system based on knuckle-based features. Neural Comput & Applic 18, 87–91 (2009). https://doi.org/10.1007/s00521-007-0160-4

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  • DOI: https://doi.org/10.1007/s00521-007-0160-4

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