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Quality Induced Multiclassifier Fingerprint Verification Using Extended Feature Set

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Signal and Image Processing for Biometrics

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 292))

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Abstract

Automatic fingerprint verification systems use ridge flow patterns and general morphological information for broad classification and minutia features for verification. With the availability of high resolution fingerprint sensors, it is now feasible to capture more intricate features such as ridges, pores, permanent scars, and incipient ridges. These fine details are characterized as level-3 or extended features and play an important role in matching and improving the verification accuracy. The main objective of this research is to develop a quality induced multi-classifier fingerprint verification algorithm that incorporates both level-2 and level-3 features. A quality assessment algorithm is developed that uses Redundant Discrete Wavelet Transform to extract edge, noise and smoothness information in local regions and encodes into a quality vector. The feature extraction algorithm first registers the gallery and probe fingerprint images using a two-stage registration process. Then, a fast Mumford-Shah curve evolution algorithm is used to extract four level-3 features namely, pores, ridge contours, dots, and incipient ridges. Gallery and probe features are matched using Mahalanobis distance measure and quality based likelihood ratio approach. Further, the quality induced sum rule fusion algorithm is used to combine the match scores obtained from level-2 and level-3 features. The experiments performed on 1000 ppi (pixels per inch) fingerprint databases show the effectiveness of the proposed algorithms.

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Notes

  1. 1.

    The size of blocks depends on the image characteristics. In our experiments, block size of \(16\times 16\) yields the best results. The RDWT decomposition is applied up to three levels.

References

  1. Ashbaugh DR (1999) Quantitative-qualitative friction ridge analysis. An introduction to basic and advanced ridgeology, CRC Press, Boca Raton

    Google Scholar 

  2. Bao P, Zhang L, Wu X (2005) Canny edge detection enhancement by scale multiplication. IEEE Trans Pattern Anal Mach Intell 27(9):1485–1490

    Article  Google Scholar 

  3. CDEFFS: the ANSI/ NIST committee to define an extended fingerprint feature set (2008) Available at http://fingerprint.nist.gov/standard/cdeffs/index.html

  4. Chan T, Vese L (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277

    Article  MATH  Google Scholar 

  5. Chapel CE (1971) Fingerprinting: a manual of identification. Coward-McCann, New York

    Google Scholar 

  6. Chen Y, Jain AK (2007) Dots and incipients: extended features for partial fingerprint matching. In: Biometric symposium, biometric consortium conference

    Google Scholar 

  7. Cowger JF (1983) Friction ridge skin: comparison and identification of fingerprints. Elsevier, New York

    Google Scholar 

  8. Duda RO, Hart PE (2000) Pattern classification. 2nd edn. Wiley, New York

    Google Scholar 

  9. Fowler E (2004) The redundant discrete wavelet transform and additive noise. Technical Report MSSU-COE-ERC-04-04, Mississippi State ERC, Mississippi State University

    Google Scholar 

  10. Galton F (1965) Finger prints (reprint). Da Capo Press, New York

    Google Scholar 

  11. Henry ER (1900) Classification and uses of fingerprints. Routledge & Sons, London

    Google Scholar 

  12. Jain AK, Flynn P, Ross A (2007) Handbook of biometrics. Springer, New York

    Google Scholar 

  13. Jain AK, Hong L, Bolle R (1997) On-line fingerprint verification. IEEE Trans Pattern Anal Mach Intell 19(4):302–314

    Article  Google Scholar 

  14. Jain AK, Chen Y, Demirkus M (2007) Pores and ridges: high resolution fingerprint matching using level 3 features. IEEE Trans Pattern Anal Mach Intell 29(1):15–27

    Article  Google Scholar 

  15. Jiang XD, Yau WY, Ser W (2001) Detecting the fingerprint minutiae by adaptive tracing the gray level ridge. Pattern Recogn 34(5):999–1013

    Google Scholar 

  16. Kryszczuk K, Drygajlo A, Morier P (2004) Extraction of level 2 and level 3 features for fragmentary fingerprints. In: Proceedings of 2nd COST275 workshop, pp 83–88

    Google Scholar 

  17. Kryszczuk K, Morier P, Drygajlo A (2004) Study of the distinctiveness of level 2 and level 3 features in fragmentary fingerprint comparison. In: Proceedings of ECCV international workshop on biometric authentication, pp 124–133

    Google Scholar 

  18. Lee HC, Gaensslen RE (1991) Advances in fingerprint technology. Elsevier, New York

    Google Scholar 

  19. Maltoni D, Maio D, Jain AK, Prabhakar S (2003) Handbook of fingerprint recognition. Springer, New York

    Google Scholar 

  20. Meenen P, Ashrafi A, Adhami R (2006) The utilization of a taylor series-based transformation in fingerprint verification. Pattern Recogn Lett 27(14):1606–1618

    Article  Google Scholar 

  21. Moenssens A (1971) Fingerprint techniques. Chilton Book Company, London

    Google Scholar 

  22. Nandakumar K, Chen Y, Dass SC, Jain AK (2008) Likelihood ratio based biometric score fusion. IEEE Trans Pattern Anal Mach Intell 30(2):342–347

    Article  Google Scholar 

  23. National Institute of Standards and Technology (1994) Guidelines for the use of advanced authentication technology alternatives. Federal Information Processing Standards Publication, p 190

    Google Scholar 

  24. Osterberg J, Parthasarathy T, Raghavan T, Sclove S (1977) Development of a mathematical formula for the calculation of fingerprint probabilities based on individual characteristic. J Am Stat Assoc 72:772–778

    Google Scholar 

  25. Pavlidis T (1982) Algorithms for graphics and image processing. Computer Science Press, Berlin

    Google Scholar 

  26. Roddy AR, Stosz JD (1997) Fingerprint features-statistical analysis and system performance estimates. Proc IEEE 85(9):1390–1421

    Article  Google Scholar 

  27. Ross A, Jain AK (2003) Information fusion in biometrics. Pattern Recogn Lett 24(13): 2115–2125

    Google Scholar 

  28. Roxburgh T (1933) On the evidential value of fingerprints. Sankhya Indian J Stat 1:189–214

    Google Scholar 

  29. Saviers K (1987) Friction skin characteristics: a study and comparison of proposed standards. California Police Department, Garden Grove

    Google Scholar 

  30. Shi Y, Karl WC (2008) A real-time algorithm for the approximation of level-set-based curve evolution. IEEE Trans Image Process 17(5):645–656

    Article  MathSciNet  Google Scholar 

  31. Stanley C (1994) Are fingerprints a genetic marker for handedness? Behav Genet 24(2):141

    Article  Google Scholar 

  32. Stoney DA (1985) A quantitative assessment of fingerprint individuality. Ph.D. Dissertation, University of California, Berkeley

    Google Scholar 

  33. Tsai A, Yezzi A Jr, Willsky AS (2001) Curve evolution implementation of the mumford-shah functional for image segmentation, denoising, interpolation, and magnification. IIEEE Trans Image Process 10(8):1169–1186

    Google Scholar 

  34. Vatsa M, Singh R, Noore A, Singh SK (2009) Combining pores and ridges with minutiae for improved fingerprint verification. Signal Process 89(12):2676–2685

    Article  MATH  Google Scholar 

  35. Zhang D, Liu F, Zhao Q, Lu G, Luo N (2011) Selecting a reference high resolution for fingerprint recognition using minutiae and pores. IEEE Trans Instrum Meas 60(3):863–871

    Article  Google Scholar 

  36. Zhao Q, Jain A (2010) On the utility of extended fingerprint features: A study on pores. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition workshops, pp 9–16

    Google Scholar 

  37. Zhao Q, Zhang L, Zhang D, Luo N (2009) Direct pore matching for fingerprint recognition. In: Proceedings of third international conference on advances in biometrics, pp 597–606

    Google Scholar 

  38. Zivkovic Z, Heijden FVD (2004) Recursive unsupervised learning of finite mixture models. IEEE Trans Pattern Anal Mach Intell 26(5):651–656

    Article  Google Scholar 

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Acknowledgments

This work was supported in part through a grant from the Department of Science and Technology (India) under FAST scheme. The author would like to thank Prof. A. Noore and Dr. R. Singh for their feedback and comments.

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Authors and Affiliations

  1. IIIT, New Delhi, India

    Mayank Vatsa

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  1. Mayank Vatsa
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Correspondence to Mayank Vatsa .

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Editors and Affiliations

  1. Federal University of Rio Grande do Sul, Porto Alegre, Brazil

    Jacob Scharcanski

  2. IT-Instituto de Telecomunicações, University of Beira Interior, Covilhã, Portugal

    Hugo Proença

  3. Indiana University-Purdue University Indianapolis, Indianapolis, Indiana, USA

    Eliza Du

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Vatsa, M. (2014). Quality Induced Multiclassifier Fingerprint Verification Using Extended Feature Set. In: Scharcanski, J., Proença, H., Du, E. (eds) Signal and Image Processing for Biometrics. Lecture Notes in Electrical Engineering, vol 292. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54080-6_9

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  • DOI: https://doi.org/10.1007/978-3-642-54080-6_9

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  • Print ISBN: 978-3-642-54079-0

  • Online ISBN: 978-3-642-54080-6

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