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Fast and Accurate Fingerprint Recognition in Principal Component Subspace

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Emerging Research in Computing, Information, Communication and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 882))

Abstract

In the case of fingerprint-based person recognition, the most widely used discriminating features are minutiae (end points and bifurcations of ridges). Majority of fingerprint matching algorithms are dealing with comparing the parameters directly derived from or relative to minutiae points extracted from the templates. Hence eventually fingerprint matching based on minutiae can be reduced to a 2D point set matching problem. Various security pitfalls like impersonation using one’s minutiae coordinates and performance issues related to enhancement as well as spurious minutiae removal are obvious in such a system. Certain non-minutiae based schemes are able to give acceptable performance at the cost of increased complexity which results in increased execution time. In order to overcome these issues, we propose a simple yet efficient and faster fingerprint alignment and matching scheme based on statistical features which will not reveal the unique local features of the template. Proposed matching technique is based on the weighted similarity score obtained by comparing the principal component subspaces of fingerprint templates. Proposed method also utilizes an alignment scheme based on principal components calculated for the 2D coordinates of fingerprint region with minimal overhead without any helper data.

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References

  1. Bahgat, G., Khalil, A., Kader, N. A., & Mashali, S. (2013). Fast and accurate algorithm for core point detection in fingerprint images. Egyptian Informatics Journal, 14(1), 15–25.

    Article  Google Scholar 

  2. Ballan, M., & Gurgen, F. (1999). On the principal component based fingerprint classification using directional images. Mathematical and Computational Applications, 4(2), 91–97.

    Article  Google Scholar 

  3. Bhattacharyya, S., & Chakraborty, S. (2014). Reconstruction of human faces from its eigenfaces. International Journal, 4(1).

    Google Scholar 

  4. Cappelli, R., Ferrara, M., & Maltoni, D. (2010). Minutia cylinder-code: A new representation and matching technique for fingerprint recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(12), 2128–2141.

    Article  Google Scholar 

  5. Damarla, S. K., & Kundu, P. (2011). Classification of unknown thermocouple types using similarity factor measurement. Sensors & Transducers, 124(1), 11.

    Google Scholar 

  6. Feng, J., & Jain, A. K. (2011). Fingerprint reconstruction: From minutiae to phase. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(2), 209–223.

    Article  Google Scholar 

  7. Ghany, K. K. A., Hassanien, A. E., & Schaefer, G. (2014). Similarity measures for fingerprint matching. In Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV) (p. 1). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp).

    Google Scholar 

  8. Hong, L., Wan, Y., & Jain, A. (1998). Fingerprint image enhancement: Algorithm and performance evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8), 777–789.

    Article  Google Scholar 

  9. Hsieh, C.-T., & Shyu, S.-R. (2007). Principal component analysis for minutiae verification on fingerprint image. In Proceedings of the 7th WSEAS International Conference on Multimedia Systems & Signal Processing, Hangzhou, China.

    Google Scholar 

  10. Jain, A. K., Prabhakar, S., Hong, L., & Pankanti, S. (2000). Filterbank-based fingerprint matching. IEEE Transactions on Image Processing, 9(5), 846–859.

    Article  Google Scholar 

  11. Johannesmeyer, M. C., Singhal, A., & Seborg, D. E. (2002). Pattern matching in historical data. AIChE Journal, 48(9), 2022–2038.

    Article  Google Scholar 

  12. Jolliffe, I. T. (1986). Principal component analysis and factor analysis. In Principal component analysis (pp. 115–128). Springer.

    Google Scholar 

  13. Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: a review and recent developments. Philosophical Transactions of the Royal Society A, 374(2065), 20150202.

    Article  MathSciNet  Google Scholar 

  14. Krzanowski, W. (1979). Between-groups comparison of principal components. Journal of the American Statistical Association, 74(367), 703–707.

    Article  MathSciNet  Google Scholar 

  15. Liao, T. W. (2005). Clustering of time series dataa survey. Pattern Recognition, 38(11), 1857–1874.

    Article  Google Scholar 

  16. Liu, E., & Cao, K. (2016). Minutiae extraction from level 1 features of fingerprint. IEEE Transactions on Information Forensics and Security, 11(9), 1893–1902.

    Article  Google Scholar 

  17. Msiza, I. S., Malumedzha, T. C., & Leke-Betechuoh, B. (2011). A novel fingerprint re-alignment solution that uses the tfcp as a reference. International Journal of Machine Learning and Computing, 1(3), 297.

    Article  Google Scholar 

  18. Mudrova, M., Prochazka, A. (2005). Principal component analysis in image processing. In Proceedings of the MATLAB Technical Computing Conference, Prague.

    Google Scholar 

  19. Nandakumar, K., & Jain, A. K. (2015). Biometric template protection: Bridging the performance gap between theory and practice. IEEE Signal Processing Magazine, 32(5), 88–100.

    Article  Google Scholar 

  20. Nandakumar, K., Jain, A. K., & Pankanti, S. (2007). Fingerprint-based fuzzy vault: Implementation and performance. IEEE Transactions on Information Forensics and Security, 2(4), 744–757.

    Article  Google Scholar 

  21. Ramoser, H., Wachmann, B., & Bischof, H. (2002). Efficient alignment of fingerprint images. In Proceedings. 16th International Conference on Pattern Recognition, 2002 (Vol. 3, pp. 748–751). IEEE.

    Google Scholar 

  22. Ross, A., Shah, J., & Jain, A. K. (2007). From template to image: Reconstructing fingerprints from minutiae points. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(4), 544–560.

    Article  Google Scholar 

  23. Rozsa, A., Glock, A. E., & Boult, T. E. (2015). Genetic algorithm attack on minutiae-based fingerprint authentication and protected template fingerprint systems. In 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (pp. 100–108). IEEE.

    Google Scholar 

  24. Shen, W., & Eshera, M. (2004). Feature extraction in fingerprint images. In Automatic Fingerprint Recognition Systems (pp. 145–181). Springer.

    Google Scholar 

  25. Wang, J., Zhu, Y., Li, S., Wan, D., & Zhang, P. (2014). Multivariate time series similarity searching. The Scientific World Journal.

    Google Scholar 

  26. Yang, J. (2011). Non-minutiae based fingerprint descriptor. InTech: In In biometrics.

    Book  Google Scholar 

  27. Yongxu, W., Xinyu, A., Yuanfeng, D., & Yongping, L. (2006). A fingerprint recognition algorithm based on principal component analysis. In TENCON 2006. 2006 IEEE Region 10 Conference (pp. 1–4). IEEE.

    Google Scholar 

  28. Zaeri, N. (2011). Minutiae-based fingerprint extraction and recognition. InTech: In biometrics.

    Book  Google Scholar 

  29. Zhang, D., Liu, F., Zhao, Q., Lu, G., & Luo, N. (2011). Selecting a reference high resolution for fingerprint recognition using minutiae and pores. IEEE Transactions on Instrumentation and Measurement, 60(3), 863–871.

    Article  Google Scholar 

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Acknowledgements

This research is supported by Kerala State Planning Board project CEPIA(2017–18).

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Authors and Affiliations

  1. Indian Institue of Information Technology and Management-Kerala, Research Centre of Cochin University of Science and Technology, Kazhakkuttom, Thiruvananthapuram, Kerala, India

    S. P. Ragendhu & Tony Thomas

Authors
  1. S. P. Ragendhu
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  2. Tony Thomas
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Corresponding author

Correspondence to S. P. Ragendhu .

Editor information

Editors and Affiliations

  1. Chancellor, Central University of Karnataka, Kalaburagi, Karnataka, India

    N. R. Shetty

  2. INSA Senior Scientist, National Institute of Advanced Studies,, Bangalore, Karnataka, India

    L. M. Patnaik

  3. Principal, Nitte Meenakshi Institute of Technology, Bangalore, Karnataka, India

    H. C. Nagaraj

  4. Professor, Nitte Meenakshi Inst of Tech, Bangalore, Karnataka, India

    Prasad Naik Hamsavath

  5. Professor, Nitte Meenakshi Institute of Technology, Bangalore, Karnataka, India

    N. Nalini

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Ragendhu, S.P., Thomas, T. (2019). Fast and Accurate Fingerprint Recognition in Principal Component Subspace. In: Shetty, N., Patnaik, L., Nagaraj, H., Hamsavath, P., Nalini, N. (eds) Emerging Research in Computing, Information, Communication and Applications. Advances in Intelligent Systems and Computing, vol 882. Springer, Singapore. https://doi.org/10.1007/978-981-13-5953-8_26

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  • DOI: https://doi.org/10.1007/978-981-13-5953-8_26

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-5952-1

  • Online ISBN: 978-981-13-5953-8

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