Abstract
Automatic personal identification system by extracting minutiae points from the thinned fingerprint image is one of the popular methods in a biometric system based on fingerprint. Due to various structural deformations, extracted minutiae points from a skeletonized fingerprint image may contain a large number of false minutiae points. This largely affects the overall matching performance of the system. The solution is to validate the minutiae points extracted and to select only true minutiae points for the subsequent matching process. This paper proposes several pre- and post-processing techniques which are used to enhance the fingerprint skeleton image by detecting and canceling the false minutiae points in the fingerprint image. The proposed method is tested on FVC2002 standard dataset and the experimental results show that the proposed techniques can remove false minutiae points.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Jiang, X., Yau, W.Y., Ser, W.: Detecting the fingerprint minutiae by adaptive tracing the gray-level ridge. Pattern Recognition. 34(5), 999–1013 (2001)
Gao, X., Chen, X., Cao, J., Deng, Z., Liu, C., feng, J.: A Novel Method Of Fingerprint Minutiae Extraction Based On Gabor Phase. In: Proc. IEEE International Conference on Image Processing, pp. 3077–3080 (2010)
Ratha, N.K., Chen, S., Jain, A.K.: Adaptive flow orientation-based feature extraction in fingerprint images. Pattern Recognition. 28(11), 1657–1672 (1995)
Zhixin Shi, Venu Govindaraju: A chaincode based scheme for fingerprint feature extraction. Pattern Recognition Letters. 27, 462–468 (2006)
Tico, M., Kuosmanen, P.: An algorithm for fingerprint image postprocessing. In: Proc. of the Thirty-Fourth Asilomar Conference on Signals Systems and Computers, pp. 1735-1739 (2000)
Zaho, F., Tang, X.: Preprocessing and postprocessing for skeleton-based fingerprint minutiae extraction. Pattern Recognition. 40(4), 1270–1281 (2007)
Kim, S., Lee, D., Kim, J.: Algorithm for detection and elimination of false minutiae in fingerprint image. In: Proc. of the Third International Conference on Audio and Video-based Biometric Person Authentication (AVBPA’01), Halmstad, Sweden, pp. 235–240 (2001)
Chikkerur, S., Cartwright, A.N., Govindaraju, V.: Fingerprint enhancement using stft analysis. Pattern Recognition. 40(1), 198–211 (2007)
Parker, J.R.: Gray level thresholding in badly illuminated images, IEEE Trans. Pattern Anal. Mach. Intell., 13(8), 813–819 (1991)
Ahmed, M., Ward, R.: A rotation invariant rule-based thinning algorithm for character recognition. IEEE Trans. Pattern Anal. Mach. Intell., 24(12), 1672–1678 (2002)
Patil, P., Suralkar, S., Sheikh, F.: Rotation invariant thinning algorithm to detect ridge bifurcations for fingerprint identification. In: 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI’05) 2005
Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of fingerprint recognition. Springer, New York (2003)
Institute of Standards and Technology, http://www.nist.gov/itl/iad/ig/fpmv.cfm (accessed on: 12/05/2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media Singapore
About this paper
Cite this paper
Zacharias, G.C., Nair, M.S., Sojan Lal, P. (2017). Pre- and Post-fingerprint Skeleton Enhancement for Minutiae Extraction. In: Raman, B., Kumar, S., Roy, P., Sen, D. (eds) Proceedings of International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 459. Springer, Singapore. https://doi.org/10.1007/978-981-10-2104-6_41
Download citation
DOI: https://doi.org/10.1007/978-981-10-2104-6_41
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-2103-9
Online ISBN: 978-981-10-2104-6
eBook Packages: EngineeringEngineering (R0)