Abstract
Singular Point (SP) is one of the local fingerprint features, and it is used as a landmark due its scale and rotation immutability. SP characteristics have been widely used as a feature vector for many fingerprint classification approaches. This paper introduces a new application of singular point location in fingerprint classification by considering it as a reference point to the partitioning process in the proposed pattern-based classification algorithm. The key idea of the proposed classification method is dividing fingerprint into small sub images using SP location, and then, creating distinguished patterns for each class using frequency domain representation for each sub-image. The performance evaluation of the SP detection and the proposed algorithm with different database sub-sets focused on both the processing time and the classification accuracy as key issues of any classification approach. The experimental work shows the superiority of using singular point location with the proposed classification algorithm. The achieved classification accuracy over FVC2002 database subsets is up to 91.4% with considerable processing time and robustness to scale, shift, and rotation conditions.
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References
Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition. Springer, Heidelberg (2009)
Liu, M.: Fingerprint Classification based on Adaboost Learning from Singularity Features. Pattern Recognition 43, 1062–1070 (2010)
Henry, E.: Classification and uses of Fingerprints. Routledge & Sons, London (1900)
Jain, A.K., Minut, S.: Hierarchical Kernel Fitting for Fingerprint Classification and Alignment. In: Proceedings of IEEE 16th International Conference on Pattern Recognition (ICPR 2002), p. 20469. IEEE, Quebec City (2002)
Cappelli, R., Maio, D., Maltoni, D.: A Multi-Classifier Approach to Fingerprint Classification. Pattern Analysis & Applications 5, 136–144 (2002)
Maltoni, D., Maio, D.: A Structural Approach to Fingerprint Classification. In: Proceedings of 13th International Conference on Pattern Recognition (ICPR). IEEE Computer Society, Vienna (1996)
Cappelli, R., Maio, D., Maltoni, D., Nanni, L.: A Two-stage Fingerprint Classification System. In: Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications (WBMA 2003), pp. 95–99. ACM, Berkley (2003)
Senior, A.: A Combination Fingerprint Classifier. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 1165–1174 (2001)
Wang, W., Li, J., Chen, W.: Fingerprint Classification Using Improved Directional Field and Fuzzy Wavelet Neural Networks. In: Proceedings of the IEEE Sixth World Congress on Intelligent Control and Automation, pp. 9961–9964. IEEE, Dalian (2006)
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)
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 IEEE International Conference on Image Processing (ICIP 2004), pp. 849–852. IEEE, Singapore (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 2007), 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 Ffiltering. Pattern Recognition Letters 24, 2135–2144 (2003)
Awad, A.I., Baba, K.: Fingerprint Singularity Detection: A Comparative Study. In: Proceeding of the Second International Conference on Software Engineering and Computer Systems (ICSECS 2011). LNCS, Springer, Kuantan (to appear, 2011)
Hou, Z., Yau, W., Wang, Y.: A Review on Fingerprint Orientation Estimation. Security and Communication Networks (2010)
Green, R.J., Fitz, A.P.: Fingerprint Classification using a Hexagonal Fast Fourier Transform. Pattern Recognition 29, 1587–1597 (1996)
Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing Using Matlab. Prentice Hall, Englewood Cliffs (2003)
Gabor, D.J.: Theory of Communication. IEE 93, 429–457 (1946)
Yang, J., Liu, L., Jiang, T., Fan, Y.: A Modified Gabor Filter Design Method for Fingerprint Image Enhancement. Pattern Recognition Letters 24, 1805–1817 (2003)
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Awad, A.I., Baba, K. (2011). An Application for Singular Point Location in Fingerprint Classification. In: Snasel, V., Platos, J., El-Qawasmeh, E. (eds) Digital Information Processing and Communications. ICDIPC 2011. Communications in Computer and Information Science, vol 188. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22389-1_24
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DOI: https://doi.org/10.1007/978-3-642-22389-1_24
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