Fingerprint Quality Assessment Combining Blind Image Quality, Texture and Minutiae Features

Z. Yao, J. Le Bars, C. Charrier, C. Rosenberger

Abstract

Biometric sample quality assessment approaches are generally designed in terms of utility property due to the potential difference between human perception of quality and the biometric quality requirements for a recognition system. This study proposes a utility based quality assessment method of fingerprints by considering several complementary aspects: 1) Image quality assessment without any reference which is consistent with human conception of inspecting quality, 2) Textural features related to the fingerprint image and 3) minutiae features which correspond to the most used information for matching. The proposed quality metric is obtained by a linear combination of these features and is validated with a reference metric using different approaches. Experiments performed on several trial databases show the benefit of the proposed fingerprint quality metric.

References

  1. Alonso-Fernandez, F., Fierrez, J., Ortega-Garcia, J., Gonzalez-Rodriguez, J., Fronthaler, H., Kollreider, K., and Bigun, J. (2007). A comparative study of fingerprint image-quality estimation methods. Information Forensics and Security, IEEE Transactions on, 2(4):734-743.
  2. Chen, T., Jiang, X., and Yau, W. (2004). Fingerprint image quality analysis. In Image Processing, 2004. ICIP 7804. 2004 International Conference on, volume 2, pages 1253-1256 Vol.2.
  3. Chen, Y., Dass, S. C., and Jain, A. K. (2005). Fingerprint quality indices for predicting authentication performance. In Audio-and Video-Based Biometric Person Authentication, pages 160-170. Springer.
  4. El Abed, M., Ninassi, A., Charrier, C., and Rosenberger, C. (2013). Fingerprint quality assessment using a noreference image quality metric. In European Signal Processing Conference (EUSIPCO), page 6.
  5. Feng, J. and Jain, A. K. (2011). Fingerprint reconstruction: from minutiae to phase. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 33(2):209-223.
  6. Grother, P. and Tabassi, E. (2007). Performance of biometric quality measures. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 29(4):531-543.
  7. Hafiane, A., Seetharaman, G., and Zavidovique, B. (2007). Median binary pattern for textures classification. In Image Analysis and Recognition, pages 387-398. Springer.
  8. Hafiane, A. and Zavidovique, B. (2006). Local relational string for textures classification. In Image Processing, 2006 IEEE International Conference on, pages 2157- 2160.
  9. Haralick, R. M., Shanmugam, K., and Dinstein, I. H. (1973). Textural features for image classification. Systems, Man and Cybernetics, IEEE Transactions on, (6):610-621.
  10. Jain, A. K., Ross, A., and Prabhakar, S. (2004). An introduction to biometric recognition. Circuits and Systems for Video Technology, IEEE Transactions on, 14(1):4- 20.
  11. Lee, B., Moon, J., and Kim, H. (2005). A novel measure of fingerprint image quality using the Fourier spectrum. In Jain, A. K. and Ratha, N. K., editors, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, volume 5779 of Society of PhotoOptical Instrumentation Engineers (SPIE) Conference Series, pages 105-112.
  12. Lim, E., Jiang, X., and Yau, W. (2002). Fingerprint quality and validity analysis. In Image Processing. 2002. Proceedings. 2002 International Conference on, volume 1, pages I-469-I-472 vol.1.
  13. Maio, D., Maltoni, D., Cappelli, R., Wayman, J. L., and Jain, A. K. (2004). Fvc2004: Third fingerprint verification competition. In Biometric Authentication, pages 1-7. Springer.
  14. Nanni, L. and Lumini, A. (2007). A hybrid waveletbased fingerprint matcher. Pattern Recognition, 40(11):3146-3151.
  15. Nanni, L., Lumini, A., and Brahnam, S. (2012). Survey on lbp based texture descriptors for image classification. Expert Systems with Applications, 39(3):3634 - 3641.
  16. Ojala, T., Pietikainen, M., and Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(7):971-987.
  17. Olsen, M. A., Xu, H., and Busch, C. (2012). Gabor filters as candidate quality measure for nfiq 2.0. In Biometrics (ICB), 2012 5th IAPR International Conference on, pages 158-163. IEEE.
  18. Pietikäinen, M. (2011). Computer vision using local binary patterns, volume 40. Springer.
  19. Ross, A. and Jain, A. (2004). Biometric sensor interoperability: A case study in fingerprints. In Biometric Authentication, pages 134-145. Springer.
  20. Ross, A., Shah, J., and Jain, A. K. (2005). Toward reconstructing fingerprints from minutiae points. In Defense and Security, pages 68-80. International Society for Optics and Photonics.
  21. Saad, M., Bovik, A. C., and Charrier, C. (2012). Blind image quality assessment: A natural scene statistics approach in the DCT domain. IEEE Transactions on Image Processing, 21(8):3339-3352.
  22. Shen, L., Kot, A., and Koo, W. (2001). Quality measures of fingerprint images. In IN: PROC. AVBPA, SPRINGER LNCS-2091, pages 266-271.
  23. Tabassi, E., Wilson, C., and Watson, C. (2004). NIST fingerprint image quality. NIST Res. Rep. NISTIR7151.
  24. Watson, C. I., Garris, M. D., Tabassi, E., Wilson, C. L., Mccabe, R. M., Janet, S., and Ko, K. (2007). User's guide to nist biometric image software (nbis).
  25. YAO, Z., Charrier, C., and Rosenberger, C. (2014). Utility validation of a new fingerprint quality metric. In International Biometric Performance Conference 2014. National Insititure of Standard and Technology (NIST).
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Paper Citation


in Harvard Style

Yao Z., Le Bars J., Charrier C. and Rosenberger C. (2015). Fingerprint Quality Assessment Combining Blind Image Quality, Texture and Minutiae Features . In Proceedings of the 1st International Conference on Information Systems Security and Privacy - Volume 1: ICISSP, ISBN 978-989-758-081-9, pages 336-343. DOI: 10.5220/0005268403360343


in Bibtex Style

@conference{icissp15,
author={Z. Yao and J. Le Bars and C. Charrier and C. Rosenberger},
title={Fingerprint Quality Assessment Combining Blind Image Quality, Texture and Minutiae Features},
booktitle={Proceedings of the 1st International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,},
year={2015},
pages={336-343},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005268403360343},
isbn={978-989-758-081-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,
TI - Fingerprint Quality Assessment Combining Blind Image Quality, Texture and Minutiae Features
SN - 978-989-758-081-9
AU - Yao Z.
AU - Le Bars J.
AU - Charrier C.
AU - Rosenberger C.
PY - 2015
SP - 336
EP - 343
DO - 10.5220/0005268403360343