Abstract
Iris recognition is a prosperous biometric method, but some technical difficulties still exist especially when applied in embedded systems. Support Vector Machine (SVM) has drawn great interests recently as one of the best classifiers in machine learning. In this paper, we develop an iris recognition system using SVM to classify the acquired features series. Even though the SVM outperforms most of other classifiers, it works slowly, which may hinder its application in embedded systems, where resources are usually limited. To make the SVM more applicable in embedded systems, we make several optimizations, including Active Learning, Kernel Selection and Negative Samples Reuse Strategy. Experimental data show that the method presented is amenable: the speed is 5 times faster and the correct recognition rate is almost the same as the basic SVM. This work makes iris recognition more feasible in embedded systems. Also, the optimized SVM can be widely applied in other similar fields.
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References
DeCoste, D., Scholkopf, B.: Training invariant support vector machines. Machine Learning 46, 161–190 (2002)
Ma, L., Wang, Y., Tan, T.: Iris recognition based on multichannel Gabor filtering. In: Proc.5th Asian Conf. Computer Vision, vol. I, pp. 279–283 (2002)
Daugman, J.G.: High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence 36, 1148–1161 (1993)
Mandelbrot, B.B.: The Fractal Geometry of Nature. Freeman, San Francisco (1982)
Gu, H., Pan, H., Wu, F., Zhuang, Y., Pan, Y.: The research of iris recognition based on self-similarity. Journal of Computer-Aided Design and Computer Graphics 16, 973–977 (2004) (in Chinese)
Vapnik, V.N.: Statistical Learning Theory. J. Wiley, New York (1998)
Collobert, R., Bengio, S.: SVMTorch: Support vector machines for large-scale regression problems. Journal of Machine Learning Research 1, 143–160 (2001)
sTong, S., Koller, D.: Support vector machine active learning with applications to text classification. Journal of Machine Learning Research, 45–66 (2001)
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© 2005 Springer-Verlag Berlin Heidelberg
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Gu, H., Zhuang, Y., Pan, Y., Chen, B. (2005). A New Iris Recognition Approach for Embedded System. In: Wu, Z., Chen, C., Guo, M., Bu, J. (eds) Embedded Software and Systems. ICESS 2004. Lecture Notes in Computer Science, vol 3605. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11535409_14
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DOI: https://doi.org/10.1007/11535409_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28128-3
Online ISBN: 978-3-540-31823-1
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