Abstract
Core point plays a vital role in fingerprint matching and classification. The fingerprint images may be of poor quality because of sensor type and user’s body condition. To detect the core point in noisy and poor quality fingerprint images, we have estimated the dominant orientation field based on principal component analysis and multi-scale pyramid decomposition to produce correct orientation field. The proposed work detects the optimal upper and lower core points using shape analysis of orientation field and binary candidate region images in fingerprints. Experiments are carried out on FVC databases and it is found that the proposed algorithm has high accuracy in locating exact core points.
T. Kathirvalavakumar–This work is Funded by University Grants Commission Major Research Project, New Delhi, INDIA.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Jiang, X., Liu, M., Kot, A.C.: Reference point detection for fingerprint recognition. In: IEEE Conference on Pattern Recognition, vol. 1, pp. 540–543 (2004)
Karu, K., Jain, A.K.: Fingerprint classification. Pattern Recogn. 29, 389–404 (1996)
Zhang, Q., Huang, K., Yan, H.: Fingerprint classification based on extraction and analysis of singularities and pseudoridges. In: Pan-Sydney Area Workshop Visual Information Processing (VIP 2001), vol. 11 (2001)
Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition. Springer, New York (2003)
Wang, S., Wang, Y.: Fingerprint enhancement in the singular point area. IEEE Signal Process. Lett. 11, 16–19 (2004)
Jain, A.K., Prabhakar, S., Hong, L.: A multichannel approach to fingerprint classification. IEEE Trans. Pattern Anal. Mach. Intell. 21, 348–359 (1999)
Wang, S., Zhang, W.W., Wang, Y.S.: Fingerprint classification by directional fields. In: Proceedings of IEEE International Conference on Multimodal Interfaces (ICMI 2002), pp. 395–398 (2002)
Maio, D., Maltoni, D.: Direct gray-scale minutiae detection in fingerprints. IEEE Trans. Pattern Anal. Mach. Intell. 19, 27–40 (1997)
Jain, A.K., Hong, L., Bolle, R.: On-line fingerprint verification. IEEE Trans. Pattern Anal. Mach. Intell. 19, 302–314 (1997)
Jain, A.K., Prabhakar, S., Hong, L., Pankanti, S.: Filterbank-based fingerprint matching. IEEE Trans. Image Process. 9, 846–859 (2000)
Yang, Y., Zulong, Z., Lin, K., Han, F.: A new method of singular points accurate localization for fingerprint. Phys. Procedia 33, 67–74 (2012)
Huang, C.Y., Liu, L.M., Hung, D.C.D.: Fingerprint analysis and singular point detection. Pattern Recogn. Lett. 28, 1937–1945 (2007)
Ignatenko, T., Kalker, T., van der Veen, M., Bazen, A.: Reference point detection for improved fingerprint matching. In: Proceedings of SPIE-IS & T Electronic Imaging, pp. 1–9 (2006)
Wrobel, K., Doroz, R.: New Method for finding a reference point in fingerprint images with the use of the IPAN99 algorithm. J. Med. Inform. Technol. 13, 59–63 (2009)
Weng, D., Yin, Y., Yang, D.: Singular points detection based on multi-resolution in fingerprint images. Neurocomputing 74, 3376–3388 (2011)
Bo, J., Ping, T.H., Lan, X.M.: Fingerprint singular point detection algorithm by poincar index. WSEAS Trans. Syst. 7, 1453–1462 (2008)
Iwasokun, G.B., Akinyokun, O.C.: Fingerprint singular point detection based on modified poincare index method. Int. J. Signal Process. Image Process. Pattern Recogn. 7, 259–272 (2014)
Fei, S., Peng, S., Bo-tao, W., An-ni, C.: Fingerprint singular points extraction based on the properties of orientation model. J. China Univ. Posts Telecommun. 18, 98–104 (2011)
Weiwei, Z., Wang, Y.: Singular point detection in fingerprint image. In: Proceedings of the 5th Asian Conference on Computer Vision (2002)
Julasayvake, A., Choomchuay, S.: An algorithm for fingerprint core point detection. In: IEEE - International Symposium on Signal Processing and its Applications, pp. 1–4 (2007)
Akram, M.U., Tariq, A., Nasir, S., Khanam, A.: Core point detection using improved segmentation and orientation. In: 6th ACS/IEEE International Conference on Computer Systems and Applications, pp. 637–644 (2008)
Kundu, M.K., Maiti, A.K.: Accurate localizations of reference points in a fingerprint image. In: Kuznetsov, S.O., Mandal, D.P., Kundu, M.K., Pal, S.K. (eds.) PReMI 2011. LNCS, vol. 6744, pp. 293–298. Springer, Heidelberg (2011)
Rahimi, M.R., Pakbaznia, E., Kasaei, S.: An adaptive approach to singular point detection in fingerprint images. Int. J. Electron. Commun. AEUE 58, 367–370 (2004)
Porwik, P., Wieclaw, L.: A new approach to reference point location in fingerprint recognition. IEICE Electron. Express 1, 1–7 (2004)
Fan, L.L., Wang, S., Guo, T.D.: Global and local information combined to detect singular points in fingerprint images. Sci. China Inf. Sci. 55, 1–13 (2012)
Awad, A.I., Baba, K.: Singular point detection for efficient fingerprint classification. Int. J. New Comput. Architectures Appl. (IJNCAA) 2, 1–7 (2012). The Society of Digital Information and Wireless Communications
Bahgat, G.A., Khalil, A.H., Kader, N.S.A., Mashali, S.: Fast and accurate algorithm for core point detection in fingerprint images. Egypt. Inform. J. 14, 15–25 (2013)
Rosa, L.: Core Point Detection Using Orthogonal Gradient Magnitudes of Fingerprint Orientation Field. http://www.advancedsourcecode.com/fingerprint.asp
Wu, C., Tulyakov, S., Govindaraju, V.: Robust point-based feature fingerprint segmentation algorithm. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 1095–1103. Springer, Heidelberg (2007)
Feng, X.G., Milanfar, P.: Multiscale principal components analysis for image local orientation estimation. In: IEEE - Signals, Systems and Computers, vol. 1, pp. 478–482 (2002)
Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition, p. 103. Springer, Heidelberg (2009)
Park, C.H., Lee, J.J., Smith, M.J.T., Park, K.H.: Singular point detection by shape analysis of directional fields in fingerprints. Pattern Recogn. 39, 839–855 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Kathirvalavakumar, T., Jeyalakshmi, K.S. (2015). Optimal Core Point Detection Using Multi-scale Principal Component Analysis. In: Prasath, R., Vuppala, A., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2015. Lecture Notes in Computer Science(), vol 9468. Springer, Cham. https://doi.org/10.1007/978-3-319-26832-3_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-26832-3_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26831-6
Online ISBN: 978-3-319-26832-3
eBook Packages: Computer ScienceComputer Science (R0)