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Nonminutiae-Based Decision-Level Fusion for Fingerprint Verification

Abstract

Most of the proposed methods used for fingerprint verification are based on local visible features called minutiae. However, due to problems for extracting minutiae from low-quality fingerprint images, other discriminatory information has been considered. In this paper, the idea of decision-level fusion of orientation, texture, and spectral features of fingerprint image is proposed. At first, a value is assigned to the similarity of block orientation field of two-fingerprint images. This is also performed for texture and spectral features. Each one of the proposed similarity measure does not need core-point existence and detection. Rotation and translation of two fingerprint images are also taken into account in each method and all points of fingerprint image are employed in feature extraction. Then, the similarity of each feature is normalized and used for decision-level fusion of fingerprint information. The experimental results on FVC2000 database demonstrate the effectiveness of the proposed fusion method and its significant accuracy.

References

  1. 1.

    Tong X, Huang J, Tang X, Shi D: Fingerprint minutiae matching using the adjacent feature vector. Pattern Recognition Letters 2005,26(9):1337–1345. 10.1016/j.patrec.2004.11.012

    Article  Google Scholar 

  2. 2.

    Zhu E, Yin J, Zhang G: Fingerprint matching based on global alignment of multiple reference minutiae. Pattern Recognition 2005,38(10):1685–1694. 10.1016/j.patcog.2005.02.016

    Article  Google Scholar 

  3. 3.

    Tico M, Kuosmanen P: Fingerprint matching using an orientation-based minutia descriptor. IEEE Transactions on Pattern Analysis and Machine Intelligence 2003,25(8):1009–1014. 10.1109/TPAMI.2003.1217604

    Article  Google Scholar 

  4. 4.

    Ghassemian H: A robust on-line restoration algorithm for fingerprint segmentation. Proceedings of IEEE International Conference on Image Processing (ICIP '96), September 1996, Lausanne, Switzerland 2: 181–184.

    Article  Google Scholar 

  5. 5.

    Hsieh C-T, Lai E, Wang Y-C: An effective algorithm for fingerprint image enhancement based on wavelet transform. Pattern Recognition 2003,36(2):303–312. 10.1016/S0031-3203(02)00032-8

    Article  Google Scholar 

  6. 6.

    Tico M, Kuosmanen P, Saarinen J: Wavelet domain features for fingerprint recognition. Electronics Letters 2001,37(1):21–22. 10.1049/el:20010031

    Article  Google Scholar 

  7. 7.

    Jain AK, Probhakar S, Hong L, Pankanti S: Filter bank-based fingerprint matching. IEEE Transactions on Image Processing 2000,9(5):846–859. 10.1109/83.841531

    Article  Google Scholar 

  8. 8.

    Lee C-J, Wang S-D: Fingerprint feature extraction using Gabor filters. Electronics Letters 1999,35(2–4):288–290.

    Article  Google Scholar 

  9. 9.

    Lee C-J, Wang S-D: Fingerprint feature reduction by principal Gabor basis function. Pattern Recognition 2001,34(11):2245–2248. 10.1016/S0031-3203(01)00029-2

    Article  Google Scholar 

  10. 10.

    Park C-H, Lee J-J, Smith MJT, Park S-I, Park K-H: Directional filter bank-based fingerprint feature extraction and matching. IEEE Transactions on Circuits and Systems for Video Technology 2004,14(1):74–85. 10.1109/TCSVT.2003.818355

    Article  Google Scholar 

  11. 11.

    Bazen AM, Verwaaijen GTB, Gerez SH, Veelenturf LPJ, Van Der Zwaag BJ: A correlation-based fingerprint verification system. Proceedings of 11th Annual Workshop on Circuits, Systems and Signal Processing (ProRISC '00), November–December 2000, Veldhoven, The Netherlands 205–213.

    Google Scholar 

  12. 12.

    Jin ATB, Ling DNC, Song OT: An efficient fingerprint verification system using integrated wavelet and Fourier-Mellin invariant transform. Image and Vision Computing 2004,22(6):503–513. 10.1016/j.imavis.2003.12.002

    Article  Google Scholar 

  13. 13.

    Sujan VA, Mulqueen MP: Fingerprint identification using space invariant transforms. Pattern Recognition Letters 2002,23(5):609–619. 10.1016/S0167-8655(01)00137-4

    Article  Google Scholar 

  14. 14.

    Gu J, Zhou J, Zhang D: A combination model for orientation field of fingerprints. Pattern Recognition 2004,37(3):543–553. 10.1016/S0031-3203(03)00178-X

    Article  Google Scholar 

  15. 15.

    Yager N, Amin A: Evaluation of fingerprint orientation field registration algorithms. Proceedings of the 17th International Conference on Pattern Recognition (ICPR '04), August 2004, Cambridge, UK 4: 641–644.

    Article  Google Scholar 

  16. 16.

    Ross A, Jain AK, Reisman J: A hybrid fingerprint matcher. Pattern Recognition 2003,36(7):1661–1673. 10.1016/S0031-3203(02)00349-7

    Article  Google Scholar 

  17. 17.

    Prabhakar S, Jain AK: Decision-level fusion in fingerprint verification. Pattern Recognition 2002,35(4):861–874. 10.1016/S0031-3203(01)00103-0

    Article  Google Scholar 

  18. 18.

    Marcialis GL, Roli F: Fusion of multiple fingerprint matchers by single-layer perceptron with class-separation loss function. Pattern Recognition Letters 2005,26(12):1830–1839. 10.1016/j.patrec.2005.03.004

    Article  Google Scholar 

  19. 19.

    Qi J, Yang S, Wang Y: Fingerprint matching combining the global orientation field with minutia. Pattern Recognition Letters 2005,26(15):2424–2430. 10.1016/j.patrec.2005.04.016

    Article  Google Scholar 

  20. 20.

    Marcials GL, Roli F: Fingerprint verification by decision-level fusion of optical and capacitive sensors. Pattern Recognition Letters 2004,25(11):1315–1322. 10.1016/j.patrec.2004.05.011

    Article  Google Scholar 

  21. 21.

    Bazen AM, Gerez SH: Systematic methods for the computation of the directional fields and singular points of fingerprints. IEEE Transactions on Pattern Analysis and Machine Intelligence 2002,24(7):905–919. 10.1109/TPAMI.2002.1017618

    Article  Google Scholar 

  22. 22.

    Maltoni D, Maio D, Jain AK, Prabhakar S: Hand Book of Fingerprint Recognition. Springer, New York, NY, USA; 2003.

    Google Scholar 

  23. 23.

    Jain AK, Nandakumar K, Ross A: Score normalization in multimodal biometric systems. Pattern Recognition 2005,38(12):2270–2285. 10.1016/j.patcog.2005.01.012

    Article  Google Scholar 

  24. 24.

    Maio D, Maltoni D, Cappelli R, Wayman JL, Jain AK: FVC2000: fingerprint verification competition. IEEE Transactions on Pattern Analysis and Machine Intelligence 2002,24(3):402–412. 10.1109/34.990140

    Article  Google Scholar 

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Correspondence to Sadegh Helfroush.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://doi.org/creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Helfroush, S., Ghassemian, H. Nonminutiae-Based Decision-Level Fusion for Fingerprint Verification. EURASIP J. Adv. Signal Process. 2007, 060590 (2006). https://doi.org/10.1155/2007/60590

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Keywords

  • Feature Extraction
  • Similarity Measure
  • Spectral Feature
  • Quantum Information
  • Visible Feature