Published May 3, 2016 | Version v1
Journal article Open

Design of Biometric Fingerprint Image Enhancement Algorithm by using Iterative Fast Fourier Transform

Creators

  • 1. RBIET

Description

Among all the minutia based fingerprint identification system, the performance depends on the quality of input fingerprint images. In this paper, we have designed and implemented an algorithm of fingerprint image enhancement by using Iterative Fast Fourier Transform (IFFT).  We have designed an approach for removing the false minutia generated during the fingerprint processing and a method to reduce the false minutia to increase the efficacy of identification system. We have used fingerprint Verification Competition 2006 (FVC 2006) as a database for implementation of proposed algorithm. Experimental results show that the results of our enhancement algorithm are better than existing algorithm of fast Fourier transform.

Files

Design_of_Biometric_Fingerprint_Image_Enhancement_Algorithm_by_using_Iterative_Fast_Fourier_Transform.pdf

Additional details

References

  • [1] Amayeh, G. B., “Improving Hand Based Verification Through Online Finger Template Update Based on Fused Confidences”, IEEE Transactions on Pattern Recognition , pp. 978-984, 2009.
  • [2] Ayers, T. F., “Modeling fingerprints: Components for thetask of synthesis”, Proceeding of Introduction to Modeling and Simulationon Biometric Technologies, pp. 155-162,Calagary, 2004
  • [3] Bolle, R. C., “Biometrics perils and patches”, Transaction of Pattern Recognition , pp. 2727-2738, 2002.
  • [4] Chikkerur, S. G., “Minutia Verification in Fingerprint Images Using Steerable Wedge Filters”, IEEE Transactions on Pattern Recognition and Machine Intelligence , pp. 45-51, 2005.
  • [5] Clarke, R., “Human Identification in Information Systems: Management Challenges and Public Policy Issues”, Information Technology & People , pp. 6-37, 1994.
  • [6] S. A. Cole, R. McNally, and K. Jordan, “Truth Machine: the Contentious History of DNA Fingerprinting”, Chicago: The University of Chicago Press, 1995.
  • [7] Kirat Pal Singh, Shivani Parmar, “VHDL Implementation of a MIPS-32 Pipeline Processor”, International Journal of Applied Engineering Research, Research India Publication, pp. 1952-1956, 2012.
  • [8] Gerez, A. M., “Systematic methods for the computation of the directional fields and singular points of fingerprints”, IEEE Transactions on Pattern Analysis and Machine Intelligence , pp. 905-919, 2002.
  • [9] Guo, G. J., “Iris Extraction Based on Intensity Gradient and Texture Difference”, IEEE workshop on Applications of Computer Vision, pp. 211-216, 2008.
  • [10] Hong, L. W., “Fingerprint Image Enhancement: Algorithm and Performance Evaluation”, Transactions on Pattern Analysis and Machine Intelligence , pp. 777-789, 1998.
  • [11] L. Hong, Y. Wan and A. K. Jain, “Fingerprint Image Enhancement: Algorithm and Performance Evaluation”, Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 8, pp. 777-789, 1998.
  • [12] Chikkerur, S. G., “Minutia Verification in Fingerprint Images Using Steerable Wedge Filters”, IEEE Transactions on Pattern Recognition and Machine Intelligence , pp. 45-51, 2005.
  • [13] Hong, L. W., “Fingerprint image enhancement:Algorithm and performance evaluation”, IEEE Transaction of Pattern Analysis and Machine Intelligence , pp. 670-781, 2000.
  • [14] Kirat Pal Singh, Shivani Parmar, Dilip Kumar, “Design of High Performance MIPS Cryptography Processor”, Proceedings of 9th International Conference Heterogeneous Networking for Quality, Reliability, Security and Robustness (GBU, Noida, U.P, India), Springer LNICST Publication, pp. 778-793, 2013.
  • [15] Jain, A. H., “Biometrics:Promisingfrontiers for emerging identification market”, ACM Communications , pp. 91-98, 2000.
  • [16] Kansaei, M. B. “Fingerprint Feature Enhancement Using Block-Direction on Reconstructed Images”, International Conference on Information, Communications and Signal Processing, pp. 721-725, 1997.
  • [17] Kukula, E. E., “Implementation of Hend Geometry at Purdue University's Recreational Center: An Analysis of User Perspectives and System Performance”, 2005.
  • [18] Maio, D. M., “FVC2002: Fingerprint verification competition”, IEEE Pattern Analysis Machine Intelligence , pp. 402-412, 2008.
  • [19] Monro, D. M., “DCT Based Iris Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence , pp. 586-593, 2007.
  • [20] Peng Wang, Q. J., “Modeling and Predicting Face Recognition System Performance Based on analysis of Similarity Scores”, IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 665-670, 2007.
  • [21] Prabhakar, S. J., “Learning Fingerprint Minutiae Location and Type”, Transactions of Pattern Recognition , pp.1847-1857, 2003.
  • [22] Ratha, N. C., “Cancelable Biometrics”, IEEE Conference on Pattern Recognition, pp. 48-51, 2006.
  • [23] Savvides, M. V., “Cancelable Biometric Filters for Face Recognition”, Proceedings of the IEEE conference on Pattern Recognition, pp. 51-54, 2004.
  • [24] Sharat, C. R., “Impact of Singular Point Detection on Fingerprint Matching Performance”, IEEE Transactions on Pattern Recognition and Machine Intelligence , pp. 64-70, 2006.
  • [25] Kirat Pal Singh, “Biometric based Network Security using MIPS cryptography Processor, International Journal of Exploration in Engineering and Technology, researchgate, pp. 33-37, 2015.
  • [26] Takahashi, K. H., “Generating Provably Secure Cancelable fingerprint Templates based on Correlation-invariant Random Filtering”, IEEE Transactions on Pattern Recognition , pp. 978-984, 2009.
  • [27] Thomas, V. C., “Learning to predict gender from iris images”, IEEE Transactions on Pattern Analysis and Machine Intelligence , pp. 11-418, 2007.
  • [28] Zuo, J. R., “Cancelable Iris Biometric”, IEEE Conference on Pattern Recognition, pp. 244-248, 2008.