×

zbMATH — the first resource for mathematics

Combining biometric fractal pattern and particle swarm optimization-based classifier for fingerprint recognition. (English) Zbl 1191.68586
Summary: This paper proposes combining the biometric fractal pattern and particle swarm optimization (PSO)-based classifier for fingerprint recognition. Fingerprints have arch, loop, whorl, and accidental morphologies, and embed singular points, resulting in the establishment of fingerprint individuality. An automatic fingerprint identification system consists of two stages: digital image processing (DIP) and pattern recognition. DIP is used to convert to binary images, refine out noise, and locate the reference point. For binary images, Katz’s algorithm is employed to estimate the fractal dimension (FD) from a two-dimensional (2D) image. Biometric features are extracted as fractal patterns using different FDs. Probabilistic neural network as a classifier performs to compare the fractal patterns among the small-scale database. A PSO algorithm is used to tune the optimal parameters and heighten the accuracy. For 30 subjects in the laboratory, the proposed classifier demonstrates greater efficiency and higher accuracy in fingerprint recognition.

MSC:
68T10 Pattern recognition, speech recognition
90C59 Approximation methods and heuristics in mathematical programming
PDF BibTeX XML Cite
Full Text: DOI EuDML
References:
[1] A. Jain and S. Pankanti, “A tough of money,” IEEE Spectrum, pp. 14-19, 2006.
[2] Y. Wang, J. Hu, and D. Phillips, “A fingerprint orientation model based on 2D fourier expansion (FOMFE) and its application to singular-point detection and fingerprint indexing,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 4, pp. 573-585, 2007. · Zbl 05340793
[3] J. P. S. Medeiros, A. C. da Cunha, A. M. Brito Jr., and P. S. M. Pires, “Automating security tests for industrial automation devices using neural networks,” in Proceedings of IEEE Symposium on Emerging Technologies and Factory Automation (ETFA ’07), pp. 772-775, September 2007.
[4] D. Bouchaffra and A. Amira, “Structural hidden Markov models for biometrics: fusion of face and fingerprint,” Pattern Recognition, vol. 41, no. 3, pp. 852-867, 2008. · Zbl 1132.68626
[5] A. M. Bazen and S. H. Gerez, “Systematic methods for the computation of the directional fields and singular points of fingerprints,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 905-919, 2002.
[6] W.-Y. Yau, J. Li, and H. Wang, “Nonlinear phase portrait modeling of fingerprint orientation,” in Proceedings of the 8th International Conference on Control, Automation, Robotics and Vision (ICARCV ’04), vol. 2, pp. 1262-1267, December 2004.
[7] M. Kamijo, “Classifying fingerprint images using neural network: deriving the classification state,” in Proceedings of IEEE International Conference on Neural Networks, vol. 3, pp. 1932-1937, March-April 1993.
[8] K. R. Crounse and L. O. Chua, “Methods for image processing and pattern formation in cellular neural networks: a tutorial,” IEEE Transactions on Circuits and Systems, vol. 42, no. 10, pp. 583-601, 1995.
[9] K.-A. Toh and W.-Y. Yau, “Combination of hyperbolic functions for multimodal biometrics data fusion,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 34, no. 2, pp. 1196-1209, 2004.
[10] J.-H. Hong, J.-K. Min, U.-K. Cho, and S.-B. Cho, “Fingerprint classification using one-vs-all support vector machines dynamically ordered with naïve Bayes classifiers,” Pattern Recognition, vol. 41, no. 2, pp. 662-671, 2008. · Zbl 1131.68513
[11] M. J. Katz, “Fractals and the analysis of waveforms,” Computers in Biology and Medicine, vol. 18, no. 3, pp. 145-156, 1988.
[12] R. Esteller, G. Vachtsevanos, J. Echauz, and B. Litt, “A Comparison of waveform fractal dimension algorithms,” IEEE Transactions on Circuits and Systems I, vol. 48, no. 2, pp. 177-183, 2001.
[13] C. T. Cheng, C. P. Ou, and K. W. Chau, “Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall-runoff model calibration,” Journal of Hydrology, vol. 268, no. 1-4, pp. 72-86, 2002.
[14] N. Muttil and K.-W. Chau, “Neural network and genetic programming for modelling coastal algal blooms,” International Journal of Environment and Pollution, vol. 28, no. 3-4, pp. 223-238, 2006.
[15] S. H. Ling, H. H. C. Iu, K. Y. Chan, H. K. Lam, B. C. W. Yeung, and F. H. Leung, “Hybrid particle swarm optimization with wavelet mutation and its industrial applications,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 38, no. 3, pp. 743-763, 2008.
[16] J. Zhang and K.-W. Chau, “Multilayer ensemble pruning via novel multi-sub-swarm particle swarm optimization,” Journal of Universal Computer Science, vol. 15, no. 4, pp. 840-858, 2009.
[17] A. Ratnaweera, S. K. Halgamuge, and H. C. Watson, “Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 240-255, 2004. · Zbl 05452076
[18] P. N. Suganthan, “Particle swarm optimizer with neighborhood operator,” in Proceedings of IEEE International Conference on Evolutionary Computation, vol. 3, pp. 1958-1962, 1999.
[19] D. Maltoni, D. Maio, A. K. Jain, and S. Prabhkar, Handbook of Fingerprint Recognition, Springer, New York, NY, USA, 2003. · Zbl 1027.68114
[20] L. Hong, Y. Wan, and A. Jain, “Fingerprint image enhancement: algorithm and performance evaluation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 8, pp. 777-789, 1998.
[21] P. Porwik and L. Wieclaw, “A new approach to reference point location in fingerprint recognition,” IEICE Electronics Express, vol. 1, no. 18, pp. 575-581, 2004.
[22] H. Guo, “A Hidden Markov Model fingerprint matching approach,” in Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC ’05), pp. 5055-5059, August 2005.
[23] M. Barnsley, Fractals Everywhere, Academic Press, Boston, Mass, USA, 1988. · Zbl 0691.58001
[24] C.-H. Lin and C.-H. Wang, “Adaptive wavelet networks for power-quality detection and discrimination in a power system,” IEEE Transactions on Power Delivery, vol. 21, no. 3, pp. 1106-1113, 2006.
[25] C.-H. Lin, Y.-C. Du, and T. Chen, “Adaptive wavelet network for multiple cardiac arrhythmias recognition,” Expert Systems with Applications, vol. 34, no. 4, pp. 2601-2611, 2008.
[26] T. L. Seng, M. Khalid, and R. Yusof, “Adaptive general regression neural network for modelling of dynamic plants,” in Proceedings of IEEE International Symposium on Intelligent Control, pp. 217-222, Vancouver, Canada, October 2002.
[27] Z.-K. Huang and K.-W. Chau, “A new image thresholding method based on Gaussian mixture model,” Applied Mathematics and Computation, vol. 205, no. 2, pp. 899-907, 2008. · Zbl 1152.68681
[28] Z. H. Cui, X. J. Cai, J. C. Zeng, and G. J. Sun, “Particle swarm optimization with FUSS and RWS for high dimensional functions,” Applied Mathematics and Computation, vol. 205, no. 1, pp. 98-108, 2008. · Zbl 1157.65383
[29] S. Chen, X. Hong, B. L. Luk, and C. J. Harris, “Non-linear system identification using particle swarm optimization tuned radial basis function models,” International Journal of Bio-Inspired Computation, vol. 1, no. 4, pp. 246-258, 2009. · Zbl 05769347
[30] S. Chen, E. S. Chng, and K. Alkadhimi, “Regularized orthogonal least squares algorithm for constructing radial basis function networks,” International Journal of Control, vol. 64, no. 5, pp. 829-837, 1996. · Zbl 0856.68120
[31] StarTek FC320 demo Program User’s Guide, StarTek Engineering, Incorpotated.
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.