Abstract
Iris recognition is a prospering biometric method, but some technical difficulties still exist. This paper proposes an iris recognition method based on selected optimal features and statistical learning. To better represent the variation details in irises, we extract features from both spatial and frequency domain. Multi-objective genetic algorithm is then employed to optimize the features. Next step is doing classification of the optimal feature sequence. SVM has recently generated a great interest in the community of machine learning due to its excellent generalization performance in a wide variety of learning problems. We modified traditional SVM as non-symmetrical support vector machine to satisfy the different security requirements in iris recognition applications. Experimental data shows that the selected feature sequence represents the variation details of the iris patterns properly.
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Gu, H., Gao, Z., Wu, F. (2005). Selection of Optimal Features for Iris Recognition. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_14
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DOI: https://doi.org/10.1007/11427445_14
Publisher Name: Springer, Berlin, Heidelberg
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