Abstract
A singular point or singularity on fingerprint is considered as a fingerprint landmark due its scale, shift, and rotation immutability. It is used for both fingerprint classification and alignment in automatic fingerprint identification systems. This paper presents a comparative study between two singular point detection methods available in the literature. The Poincaré index method is the most popular approach, and the complex filter is another proposed method applied on the complex directional images. The maximum complex filter response is highly related to the regions with abrupt changes in the ridge orientations. These regions have a high probability to contain a singular point. The optimum detection method in both processing time and detection accuracy will be updated to suite our efficient classification method. The experimental evaluation for both methods proves that the accuracy achieved by complex filter is up to 95% with considerable processing time compared to 90% with Poincaré index method.
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 subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Wang, L., Dai, M.: Application of a new type of singular points in fingerprint classification. Pattern Recognition Letters 28, 1640–1650 (2007)
Klimanee, C., Nguyen, D.T.: Classification of Fingerprints Using Singular Points and Their Principal Axes. In: Proceedings of 2004 IEEE Consumer Communications and Networking Conference (CCNC 2004): Consumer Networking, IEEE, Las Vegas (2004)
Zhanga, Q., Yan, H.: Fingerprint Classification based on Extraction and Analysis of Singularities and Pseudo Ridges. Pattern Recognition 37, 2233–2243 (2004)
Sarbadhikari, S.N., Basak, J., Pal, S.K., Kundu, M.K.: Noisy Fingerprints Classification with Directional Based Features Using MLP. Neural Computing & Applications 7, 180–191 (1998)
Kristensen, T., Borthen, J., Fyllingsnes, K.: Comparison of neural network based fingerprint classification techniques. In: International Joint Conference on Neural Networks (IJCNN), pp. 1043–1048. IEEE, Orlando (2007)
Kawagoe, M., Tojo, A.: Fingerprint pattern classification. Pattern Recognition 17, 295–303 (1984)
Nilsson, K., Josef, B.: Localization of corresponding points in fingerprints by complex filtering. Pattern Recognition Letters 24, 2135–2144 (2003)
Awad, A.I., Baba, K.: Toward An Efficient Fingerprint Classification. In: Albert, M. (ed.) Biometrics - Unique and Diverse Applications in Nature, Science, and Technology. InTech (2011)
Awad, A.I., Mustafaa, M., Moness, M.: A New Fingerprint Classification Approach Based on Fast Fourier Transformer. In: Proceedings of the 5th International Conference on Informatics and Systems. Faculty of Computers & Information, pp. 78–83. Cairo University, Cairo (2008)
Liu, M., Jiang, X., Kot, A.C.: Fingerprint Reference-Point Detection. EURASIP Journal on Applied Signal Processing, 498–509 (2005)
Parka, C.-H., Leeb, J.-J., Smitha, M.J.T., Parkc, K.-H.: Singular Point Detection by Shape Analysis of Directional Fields in Fingerprints. Pattern Recognition 39, 839–855 (2006)
Liu, M.: Fingerprint classification based on Adaboost learning from singularity features. Pattern Recognition 43, 1062–1070 (2010)
Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing Using Matlab. Prentice Hall, Englewood Cliffs (2003)
Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition. Springer, Heidelberg (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Awad, A.I., Baba, K. (2011). Fingerprint Singularity Detection: A Comparative Study. In: Mohamad Zain, J., Wan Mohd, W.M.b., El-Qawasmeh, E. (eds) Software Engineering and Computer Systems. ICSECS 2011. Communications in Computer and Information Science, vol 179. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22170-5_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-22170-5_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22169-9
Online ISBN: 978-3-642-22170-5
eBook Packages: Computer ScienceComputer Science (R0)