Quality Controlled Region-Based Partial Fingerprint Recognition

Authors

  • Omid Zanganeh Independent consultant, Melbourne, Australia
  • Komal Komal Faculty of Information Technology, Monash University, Melbourne, Australia
  • Nandita Bhattacharjee Faculty of Information Technology, Monash University, Melbourne, Australia
  • David Albrecht Faculty of Information Technology, Monash University, Melbourne, Australia
  • Bala Srinivasan Faculty of Information Technology, Monash University, Melbourne, Australia

DOI:

https://doi.org/10.13052/1550-4646.1421

Keywords:

Partial Fingerprints, Alignment, Region-based, Quality, Similarity Measure, Recognition

Abstract

The conventional method of fingerprint alignment using reference points does not work well for partial fingerprints due to the limited or non-availability of reference points. Moreover, matching of partial fingerprints using existing techniques is challenging as partial fingerprints lack enough distinguishing information. Even if fingerprints consists of sufficient information, the varying quality of different parts of fingerprint affects recognition process. In this paper, a new paradigm in the form of region-based approach that uses all available fingerprint ridge structure for aligning the fingerprints is proposed. Additionally, a new metric to compute individual local region similarity based on region’s quality, size and consistency of its neighbouring regions is proposed and used in deriving the global similarity for matching process. Although the proposed approach is computationally intensive, yet, the error rate is close to zero as the experimental results shows. The method is most suitable in applications where perfect identification is required such as forensic investigations.

 

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References

Zhao, Q., Zhang, D., Zhang, L., and Luo, N. (2010). High resolution

partial fingerprint alignment using pore–valley descriptors. Pattern

Recognition, 43(3), 1050–1061.

Khalil, M. S., Muhammad, D., Khan, M. K., and Qais, A. N. (2009).

Fingerprint verification using fingerprint texture. In International Symposium

on Signal Processing and Information Technology (ISSPIT),

–594. doi:10.1109/ISSPIT.2009.5407549

Jea, T.Y., and Govindaraju,V. (2005).Aminutia-based partial fingerprint

recognition system. Pattern Recognition, 38(10), 1672–1684.

Maltoni, D., Maio, D., Jain, A. K., and Prabhakar, S. (2009). Handbook

of Fingerprint Recognition, 2nd Edition, Springer: New York.

Wang, L., Bhattacharjee, N., and Srinivasan, B. (2011).Anovel technique

for singular point detection based on poincaré index. In Proceedings of

the 9th International Conference on Advances in Mobile Computing and

Multimedia, 12–18.

Bennamoun, M. and Mamic, G. J. (2002). Object Recognition: Fundamentals

and Case Studies, Springer.

Chen, Y., Dass, S. C., Jain, A. K. (2005). Fingerprint Quality Indices

for Predicting Authentication Performance, Lecture Notes in Computer

Science, Springer, 160–170.

Cappelli, R., Ferrara, M., and Maltoni, D. (2010). Minutia cylinder-code:

A new representation and matching technique for fingerprint recognition.

IEEE Transactions on Pattern Analysis and Machine Intelligence,

(12), 2128–2141.

Yoo, J. C., and Han, T. H. (2009). Fast normalized cross-correlation.

Circuits, Systems and Signal Processing, 28(6), 819–843.

Agarwal, B. (2007). Programmed Statistics (Question-Answers), 2nd

Edition, New Age International Ltd.

Fisher, R. A. (1921). On the probable error of a coefficient of correlation

deduced from a small sample. Metron, 1, 3–32.

Maio, D., Maltoni, D., Cappelli, R.,Wayman, J. L., and Jain,A. K. (2002).

FVC 2002: Second fingerprint verification competition. In Proceeding

of the 16th International Conference on Pattern Recognition, 3, 811–814.

Kovacs-Vajna, Z. M. (2000). A fingerprint verification system based on

triangular matching and dynamic time warping. IEEE Transactions on

Pattern Analysis and Machine Intelligence, 22(11), 1266–1276.

Tico, M., and Kuosmanen, P. (2003). Fingerprint matching using an

orientation-based minutia descriptor. IEEE Transactions on Pattern

Analysis and Machine Intelligence, 25(8), 1009–1014.

Liu, N., Yin, Y., and Zhang, H. (2005). A fingerprint matching algorithm

based on Delaunay triangulation net. In the Fifth International

Conference on Computer and Information Technology (CIT), 591–595.

Gao, Z., You, X., Zhou, L., and Zeng,W. (2011).Anovel matching technique

for fingerprint recognition by graphical structures. In International

Conference on Wavelet Analysis and Pattern Recognition (ICWAPR),

–82.

Sha, L., Zhao, F., and Tang, X. (2003). Improved fingercode for

filterbank-based fingerprint matching. In International Conference on

Image Processing (ICIP), 2, 895–898.

Zhang,Y.,Yang, X., Su, Q., and Tian, J. (2007). Fingerprint Recognition

Based on Combined Features, Vol. 4642 of Lecture Notes in Computer

Science, Springer: Berlin, Heidelberg, 281–289.

Lumini, A., and Nanni, L. (2006). Two-class fingerprint matcher. Pattern

Recognition, 39(4), 714–716.

Qader, H. A., Ramli, A. R., and Al-Haddad, S. (2007). Fingerprint

Recognition Using Zernike Moments. Int. Arab J. Inf. Technol., 4(4),

–376.

Benhammadi, F., Amirouche, M. N., Hentous, H., Beghdad, K. B., and

Aissani, M. (2007). Fingerprint matching from minutiae texture maps.

Pattern Recognition, 40(1), 189–197.

Abraham, J. Gao, J. Kwan, P. (2011). Fingerprint Matching Using

a Hybrid Shape and Orientation Descriptor, INTECH Open Access

Publisher.

Yang, J. C., Shin, J. W., and Park, D. S. (2006). Fingerprint matching

using invariant moment features. In International Conference on Computational

and Information Science, (pp. 1029–1038). Springer: Berlin,

Heidelberg.

Vijayaprasad, P., Sulaiman, M. N., Mustapha, N., and Rahmat, R.W. O.

K. (2010). Partial fingerprint recognition using support vector machine.

Information Technology Journal, 9(4), 844–848.

Abraham, J. Fingerprint matching algorithm using shape context and

orientation descriptors, access date: 14 March 2015 (2011).Available at:

http://au.mathworks.com

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Published

2018-04-26

How to Cite

Zanganeh, O., Komal, K. ., Bhattacharjee, N. ., Albrecht, D. ., & Srinivasan, B. . (2018). Quality Controlled Region-Based Partial Fingerprint Recognition. Journal of Mobile Multimedia, 14(2), 123–156. https://doi.org/10.13052/1550-4646.1421

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