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Rotation-invariant fingerprint matching using radon and DCT

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Abstract

A new set of promising rotation-invariant features based on radon and discrete cosine transform (DCT) is proposed for fingerprint matching. The radon and DCT of a tiny area in the region of core point of fingerprint image is computed. In the proposed method only 34% DCT coefficients are used for feature extraction. Competency of this approach is tested on standard databases, namely FVC2002 and FVC2004. This approach provides 70% genuine acceptance rate (GAR) at ~0% false acceptance rate (FAR) and 95% GAR at 10% FAR on rotated and non-rotated databases, respectively. Experimental results prove that the proposed feature extraction approach is rotation invariant.

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References

  1. Maltoni D, Maio D, Jain A and Prabhakar S 2003 Handbook of fingerprint recognition. New York: Springer

    MATH  Google Scholar 

  2. Jain A, Prabhakar S, Hong L and Pankanti S 2000 Filterbank-based fingerprint matching. IEEE Trans. Image Process. 9: 846–859

    Article  Google Scholar 

  3. Bharkad S and Kokare M 2012 Fingerprint matching using M band wavelet transform. In: Proceedings of the IEEE Conference on Advances in Engineering, Science and Management (ICAESM), pp. 26–32

  4. Bharkad S and Kokare M 2013 Fingerprint matching using discrete wavelet packet transform. In: Proceedings of the IEEE Conference on Advanced Computing (AC), pp. 1183–1188

  5. Bharkad S and Kokare M 2012 Rotated wavelet filter based fingerprint matching. Int. J. Pattern Recogn. Artif. Intell. 26(3): 1–21

    Article  Google Scholar 

  6. Bazen A and Gerez S 2003 Fingerprint matching by thin-plate spline modeling of elastic deformations. Pattern Recogn. 36(8): 1859–1867

    Article  Google Scholar 

  7. Jain A, Hong L and Bolle R 1997 On-line fingerprint verification. IEEE Trans. Pattern Anal. Mach. Intell. 19: 302–314

    Article  Google Scholar 

  8. Bharkad S and Kokare M 2011 Fingerprint identification: ideas, influences, and trends of the new age. In: Pattern recognition, machine intelligence and biometrics (PRMI), Chapter 17, Springer, pp. 417–455

  9. Barman S, Chattopadhyay S, Samanta D, Bag S and Show G 2014 An efficient fingerprint matching approach based on minutiae to minutiae distance using indexing with effectively lower time complexity. In: Proceedings of the IEEE Conference on Information Technology (ICIT), pp. 179–183

  10. Chu T T and Chiu C T 2016 A cost-effective minutiae disk code for fingerprint recognition and its implementation. In: Proceedings of the IEEE Conference on Acoustic, Speech and Signal Processing (ICASSP), pp. 981–985

  11. Jiang L, Zhao T, Bai C, Yong A and Wu M 2016 Direct fingerprint minutiae extraction approach based on convolutional neural networks. In: Proceedings of the IEEE Conference on Neural Networks (IJCNN), pp. 571–578

  12. Nagaty K 2004 An energy-based fingerprint matching system. In: Proceedings of the IEEE Conference on Consumer Communications and Networking, pp. 706–709

  13. Jain A, Prabhakar S, Hong L and Pankanti S 1999 FingerCode: a filterbank for fingerprint representation and matching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 187–193

  14. Ross A, Jain A and Reisman J 2002 A hybrid fingerprint matcher. In: Proceedings of the IEEE, pp. 795–798

  15. Horton M, Meenen P, Adhami R and Cox P 2002 The costs and benefits of using complex 2D Gabor filter in a filter based fingerprint matching system. In: Proceedings of the IEEE Conference on System Theory, pp. 171–175

  16. Munir M and Javed M 2005 Fingerprint matching using ridge patterns. In: Proceedings of the IEEE Conference on Information and Communication Technology, pp. 116–120

  17. Bharkad S and Kokare M 2012 Hartley transform based fingerprint matching. Int. J. Inf. Process. 8(1): 85–100

    Article  Google Scholar 

  18. Baldi P and Chauvin Y 1993 Neural networks for fingerprint recognition. Neural Comput. 5(3): 402–418

    Article  Google Scholar 

  19. Song Q, Liu X and Yang L 2015 The random forest classifier applied in droplet fingerprint recognition. In: Proceedings of the IEEE Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp. 722–726

  20. Gu F, Wang Y and Cheung Y 2014 A supervised correlation analysis for score-level calibration of cross-device fingerprint recognition. In: Proceedings of the IEEE Conference on Systems, Man, and Cybernetics, pp. 1165–1170

  21. Jain A K, Arora S S, Cao K, Best-Rowden L and Bhatnagar A 2016 Fingerprint recognition of young children. IEEE Trans. Inf. Forens. Secur. 12(7): 1501–1514

    Article  Google Scholar 

  22. Ctirad S and Christoph B 2014 Presentation attack detection methods for fingerprint recognition systems: a survey. IET Biometr. 3(4): 219–233

    Article  Google Scholar 

  23. Yu X, Xiong Q, Luo Y, Wang N, Wang L, Tey H L and Liu L 2017 Contrast enhanced subsurface fingerprint detection using high-speed optical coherence tomography. IEEE Photon. Technol. Lett. 29(1): 70–73

    Article  Google Scholar 

  24. Kim W 2017 Fingerprint liveness detection using local coherence patterns. IEEE Signal Process. Lett. 24(1): 51–55

    Article  Google Scholar 

  25. Kumar A and Kwong C 2015 Towards contactless, low-cost and accurate 3D fingerprint identification. IEEE Trans. Pattern Anal. Mach. Intell. 37(3): 681–696

    Article  Google Scholar 

  26. Kulkarni J V, Patil B D and Holambe R S 2006 Orientation feature for fingerprint matching. Pattern Recogn. 39(8): 1551–1554

    Article  MATH  Google Scholar 

  27. Sandhan T, Chang H J and Choi J Y 2013 Abstracted radon profiles for fingerprint recognition. In: Proceedings of the IEEE Conference on Image Processing, pp. 4156–4160

  28. Ray T and Dutta P K 2014 Texture classification by Rotational Invariant DCT Masks (RIDCTM) features. In: Proceedings of the IEEE Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 2041–2044

  29. Jadhav D V and Holambe R S 2009 Feature extraction using Radon and wavelet transforms with application to face recognition. Neurocomputing 72(7–9): 1951–1959

    Article  Google Scholar 

  30. Ajmera P K, Jadhav D V and Holambe R S 2011 Text-independent speaker identification using Radon and discrete cosine transforms based features from speech spectrogram. Pattern Recogn. 44(10–11): 2749–2759

    Article  Google Scholar 

  31. Toft P 1996 The Radon transform: theory and implementation. Ph.D. Thesis, Technical University of Denmark

  32. Gonzalez R C and Woods R E 1990 Digital image processing. 2nd edn, Upper Saddle River, New Jersey: Pearson Education

    Google Scholar 

  33. Jain A 1989 Fundamentals of digital image processing. Englewood Cliffs, NJ: Prentice-Hall

    MATH  Google Scholar 

  34. Bharkad S and Kokare M 2011 Performance evaluation of distance metrics: application to fingerprint recognition. Int. J. Pattern Recogn. Artif. Intell. 25(6): 777–806

    Article  MathSciNet  Google Scholar 

  35. Gowthami A T and Mamatha H R 2015 Fingerprint recognition using zone based linear binary patterns. Proc. Comput. Sci. 58: 552–557

    Article  Google Scholar 

  36. Cappelli R, Ferrara M and Maltoni D 2010 Minutiae cylinder-code: a new representation and matching technique for fingerprint recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32(12): 2128–2141

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Telecommunication, Government College of Engineering, Osmanpura, Aurangabad, 431005, India

    Sangita Bharkad & Manesh Kokare

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  1. Sangita Bharkad
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  2. Manesh Kokare
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Correspondence to Sangita Bharkad.

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Bharkad, S., Kokare, M. Rotation-invariant fingerprint matching using radon and DCT. Sādhanā 42, 2025–2039 (2017). https://doi.org/10.1007/s12046-017-0752-3

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  • DOI: https://doi.org/10.1007/s12046-017-0752-3

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