Abstract
In this paper, a combination of fast and cooperative modular neural nets to enhance the performance of the detection process is introduced. We have applied such approach successfully to detect human faces in cluttered scenes [10]. Here, this technique is used to identify human irises automatically in a given image. In the detection phase, neural nets are used to test whether a window of 20x20 pixels contains an iris or not. The major difficulty in the learning process comes from the large database required for iris / noniris images. A simple design for cooperative modular neural nets is presented to solve this problem by dividing these data into three groups. Such division results in reduction of computational complexity and thus decreasing the time and memory needed during the test of an image. Simulation results for the proposed algorithm show a good performance.
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References
Wildes, R. P.: Iris Recognition: An Emerging Biometrie Teehnology. Proceedings of IEEE, Vol. 85, No.9, pp.1347–1363, September, (1997).
Jacobs, R, Jordan, M., and Barto, A.: Task Decomposition Through Competition in a Modular Connectionist Architecture: The what and where vision tasks. Neural Computation 3, pp. 79–87, (1991).
Auda, G., Kamel, M., Raafat, H.: Voting Schemes for cooperative neural network classifiers. IEEE International Conference on Neural Networks ICNN95, Vol. 3, Perth, Australia, pp. 1240–1243, November, (1995).
Klette, R, and Zamperon.: Handbook of image processing operators. John Wiley & Sonsltd, (1996).
Auda, G. and Kamel, M.: CMNN: Cooperative Modular Neural Networks for Pattern Recognition. Pattern Recognition Letters, Vol. 18, pp. 1391–1398, (1997).
Alpaydin, E.: Multiple Networks for Function Learning. Int. Conf. on Neural Networks, Vol.1 CA, USA, pp. 9–14, (1993).
Waibel, A.: “Modular Construction of Time Delay Neural Networks for Speech Recognition”. Neural Computing 1, pp. 39–46, (1989).
K. Joe, Y. Mori, and S. Miyake.: Construction of a large scale neural network: Simulation of handwritten Japanese Character Recognition. on NCUBE Concurrency 2(2), pp. 79–107, (1990).
Jain, A., Bolle, R., and Pankanti, S.: BIOMETRICS: Personal Identification in Networked Society. Chap5, pp. 103–122, Kluwer Academic Publishers, (1998).
El-Bakry, H. M., Abo-Elsoud, M. A., and Kamel, M. S.: Fast Modular Neural Networks for Human Face Detection. Proc. of IJCNN International Joint Conference on Neural Networks 2000, Corno, Italy, Vol. III, pp. 320–323, 24-27 July, (2000).
Ben-Yacoub, S.: Fast Object Detection using MLP and FFT. IDIAP-RR 11, IDIAP, (1997).
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El-Bakry, H.M. (2001). Fast Iris Detection Using Cooperative Modular Neural Nets. In: Kůrková, V., Neruda, R., Kárný, M., Steele, N.C. (eds) Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6230-9_49
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DOI: https://doi.org/10.1007/978-3-7091-6230-9_49
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83651-4
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