Abstract
In this paper, a modified radial basis function network (RBFN) based on self-organization mapping (SOM) has been designed and developed for rotation invariant clear as well as occluded fingerprint recognition. The SOM-based RBFN learns different fingerprint images and performs subsequent rotation invariant recognition of clear and occluded images. The system is efficient, effective, and fast. Also, the performance evaluation of the system is substantially moderate.
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References
Bana, S., Kaur, D.: Fingerprint recognition using image segmentation. IJAEST 5(1), 012–023 (2011)
Kovâcs-Vajna, Z.M.: A fingerprint verification system based on triangular matching and dynamic time warping. IEEE Trans. PAMI 22(11), 1266–1276 (2000)
Chatterjee, A., Mandal, S., Rahaman, G.M.A., Arif, A.S.M.: Fingerprint identification and verification system by minutiae extraction using artificial neural network. JCIT 1(1), 12–16 (2010)
Astrov, I., Tatarly, S., Tatarly, S.: Fingerprint recognition for varied degrees of image distortion using threerate hybrid kohonen neural network. In: ICALIP, pp. 363–369 (2008)
Turky, A.M., Ahmad, M.S.: The use of SOM for fingerprint classification. In: International Conference on Information Retrieval and Knowledge Management, pp. 287–290 (2010)
Kundu, S., Sarker, G.: A modified radial basis function network for occluded fingerprint identification and localization. IJCITAE 7(2), 103–109 (2013)
Sarker, G., Kundu, S.: A modified radial basis function network for fingerprint identification and localization. In: International Conference on Advanced Engineering and Technology, pp. 26–31 (2013)
Kundu, S., Sarker, G.: A modified RBFN based on heuristic based clustering for location invariant fingerprint recognition and localization with and without occlusion. In: IEEE’s International Conferences for Convergence of Technology (2014)
Yegnanarayana, B.: Artificial Neural Networks, pp. 223–228. PHI Learning Private Limited, Delhi, (1999)
Aziz, K.A.A., Ramlee, R.A., Abdullah, S.S., Jahari, A.N.: Face detection using radial basis function neural networks with variance spread value. In: International Conference of Soft Computing and Pattern Recognition, pp. 399–403 (2009)
Ashok, J., Rajan, E.G.: Off-line hand written character recognition using radial basis function. Int. J. Adv. Network. Appl. 2(2), 792–795 (2011)
Liu, X., Geng, G., Wang, X.: Automatically face detection based on bp neural network and bayesian decision. In: 2010 Sixth International Conference on Natural Computation, pp. 1590–1594 (2010)
Sarker, G.: A back propagation network for face identification and localization. In: International Conference on Recent Trends in Information Systems, pp. 24–29 (2011)
Pudi, V.: Data mining. Oxford University Press India, Oxford (2009)
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Kundu, S., Sarker, G. (2015). A Modified SOM-Based RBFN for Rotation Invariant Clear and Occluded Fingerprint Recognition. In: Mandal, D., Kar, R., Das, S., Panigrahi, B. (eds) Intelligent Computing and Applications. Advances in Intelligent Systems and Computing, vol 343. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2268-2_2
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DOI: https://doi.org/10.1007/978-81-322-2268-2_2
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