A Novel Method for Iris Recognition Using BP Neural Network and Parallel Computing
Abstract
In this paper, we seek a new method in designing an iris recognition system. In this method, first the Haar wavelet features are extracted from iris images. The advantage of using these features is the high-speed extraction, as well as being unique to each iris. Then the back propagation neural network (BPNN) is used as a classifier. In this system, the BPNN parallel algorithms and their implementation on GPUs have been used by the aid of CUDA in order to speed up the learning process. Finally, the system performance and the speeding outcomes in a way that this algorithm is done in series are presented.
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K. W. Bowyer, P. H. Karen, and J. F. Patrick, "A survey of iris biometrics research: 2008–2010." Handbook of iris recognition. Springer London, 2013.
R. J. Matey, and R. K. Lauren, "Iris recognition–beyond one meter." Handbook of Remote Biometrics. Springer London, 2009.
P. N. Sandipan, S. N. Abhilasha and M. W. Laxman "Iris Based Recognition System Using Wavelet Transform" IJCSNS International Journal of Computer Science and Network Security, Vol.9, No.11, 2009.
J. G. Daugman, “High confidence visual recognition of persons by a test of statistical independence,” IEEE Trans on Pattern Analysis and Machine Intelligence, vol. 15, no. 11, 1993, pp. 1148–1161.
J. G. Daugman, “New methods in iris recognition,” IEEE Trans on Systems, Man and Cybernetics, part B: Cybernetics, vol. 37,no. 5, 2007, pp. 1167–1175.
R. P. Wildes, “Iris recognition: An emerging biometric technology,” Proc of the IEEE, vol. 85, no. 9, 1997, pp. 1348–1363.
W. W. Boles and B. Boashash, “A human identification technique using images of the iris and wavelet transform,” IEEE Trans on Signal Processing, vol. 46, no. 4,1998, pp. 1185–1188.
S. Lim, K. Lee, O. Byeon, and T. Kim, “Efficient iris recognition through improvement of feature vector and classifier,” ETRI Journal, vol. 23, no. 2, 2001, pp. 61–70,.
O. AL-Allaf, N. Ahmad, A. Shahlla, A. Kader, and A. A. Tamimi. "Pattern Recognition Neural Network for Improving the Performance of Iris Recognition System." Journal of Scientific and Engineering Research 4.6 . 2013.
F. Jan, "Iris localization based on the Hough transform, a radial-gradient operator, and the gray-level intensity." Optic-International Journal for Light and Electron Optics 124.23. 2013.
M. Askari,, "Parallel GPU Implementation of Hough Transform for Circles." International Journal of Computer Science Issues 10.2 . 2013.
H. T. Ngo, "Resource-aware architecture design and implementation of Hough transform for a real-time iris boundary detection system." Consumer Electronics, IEEE Transactions on 60.3. 2014.
R. Wildes, “Iris Recognition: An Emerging Biometric Technology”, Proceedings of the IEEE, vol. 85,1999. pp 1348-1363.
S. R. Patnala and R. Chandra, "Iris Recognition System Using Fractal Dimensions of Haar Patterns" International Journal of Signal Processing, Image Processing and Pattern Recognition Vol.2, No.3,September 2009
S. Shylaja , ”Feed Forward Neural Network Based Eye Localization and Recognition Using Hough Transform”, International Journal of Advance Computer Science and Applications, Vol.2, No.3, 2011. pp.104-108.
Y. Zhiyi, "Parallel Image Processing Based on CUDA",2008. pp. 198-201.
M. Askari, "Performance Improvement of Lucy-Richardson Algorithm using GPU " presented at the Machine Vision and Image Processing (MVIP), Esfahan, 2010.
S. Grauer-Gray, "GPU implementation of belief propagation using CUDA for cloud tracking and reconstruction," presented at the Pattern Recognition in Remote Sensing (PRRS 2008), 2008.
P. Ghosh,, and M. Rajashekharababu. "Authentication using Iris Recognition with Parallel Approach." International Journal of Computer Science and Network Security (IJCSNS) 13.5. 2013.