Abstract
The important step in automatic fingerprint recognition system is to automatically and reliably extract minutia from the input fingerprint images. In such recognition systems, the orientation of the fingerprint image has influence on fingerprint image enhancement phase, minutia detection phase and minutia matching phase of the system. The fingerprint image rotation, translation, and registration are the commonly used techniques, to minimize the error in all these stages of fingerprint recognition. In this work, we approached two methods by which the minutia is detected. In the first method, the minutias are detected without aligning the image. In the second method, the input image is aligned using the proposed k-means clustering based fingerprint image rotation algorithm and then the minutias are detected. This proposed rotation algorithm could be applied as a pre-processing step before minutia detection. In both the methods the images are enhanced using the proposed Gabor filter. Finally the results clearly show that the aligned images give more accurate true minutias then the unaligned images. Hence, the result will be better detection of minutia as well as better matching with improved performance.
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References
Hong, L., Wan, Y., Jain, A.K.: Fingerprint image enhancement: Algorithm and performance evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 777–789 (1998)
Jain, A.K., Hong, L., Pankanti, S., Bolle, R.: An Identity authentication system using fingerprints. Proc. IEEE 85(9), 1365–1388 (1997)
Jain, A.K., Hong, L., Pankanti, S., Bolle, R.: On-Line fingerprint verification. IEEE Trans. Pattern Anal. Mach. Intell. 19(4), 302–314 (1997)
Jiang, X., Yau, W.: Fingerprint minutia matching based on the local and global structures. In: Proc. 15th Int. Conf. Pattern Recognition, Barcelona, Spain, vol. 2, pp. 1042–1045 (September 2000)
Lee, H.C., Gaensslen, R.E. (eds.): Advances in Fingerprint Technology. Elsevier, New York (1991)
Maio, D., Maltoni, D., Cappelli, R., Wayman, J.L., Jain, A.K.: FVC2000: Fingerprint Verification Competition. In: 15th IAPR International Conference on Pattern Recognition, Barcelona, Spain, September 3-7 (2000), http://bias.csr.unibo.it/fvc2000/
Pankanti, S., Prahakar, S., Jain, A.K.: On the individuality of fingerprints. IEEE Trans, Pattern and Mach. Intell., 1010–1025 (2002)
Ratha, N., Chen, S., Jain, A.: An Adaptive flow orientation based feature extraction in fingerprint images. Pattern Recognition, 1657–1672 (1995)
Singh, R., Shah, U., Gupta, V.: Fingerprint Recognition, Student project, Department of Computer Science and Engineering, Indian Institute of Technology, Kanpur, India (November 2009)
John, V.C.: Fingerprint Based Authentication System, Correlation Based Classification Approach, MSITRT, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA
Bhanu, X.T., Yingqiang Lin, B.: Fingerprint classification based on learned features. Center for Res. in Intelligent Syst., Univ. of California, Riverside, CA (2005)
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© 2011 Springer-Verlag Berlin Heidelberg
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Jaganathan, P., Rajinikannan, M. (2011). Reducing False Positives in Minutia Detection by Using the Proposed Fingerprint Alignment Technique. In: Nagamalai, D., Renault, E., Dhanuskodi, M. (eds) Advances in Digital Image Processing and Information Technology. DPPR 2011. Communications in Computer and Information Science, vol 205. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24055-3_21
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DOI: https://doi.org/10.1007/978-3-642-24055-3_21
Publisher Name: Springer, Berlin, Heidelberg
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