Abstract
In different methods of Biometrics, recognition by iris images in recent years has been taken into consideration by researchers as one of the common methods of identification like passwords, credit cards or keys. Iris recognition a new biometric technology has great advantages such as variability, stability and security. In this paper we propose a new feature extraction method for iris recognition based on contourlet transform. Contourlet transform captures the intrinsic geometrical structures of iris image. It decomposes the iris image into a set of directional sub-bands with texture details captured in different orientations at various scales so for reducing the feature vector dimensions we use the method for extract only significant bit and information from normalized iris images. In this method we ignore fragile bits. At last the feature vector is approximated by non linear approximation coefficient. Experimental results show that the proposed method reduces processing time and increase the classification accuracy and outperforms the wavelet based method.
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Azizi, A., Pourreza, H.R. (2009). A New Method for Iris Recognition Based on Contourlet Transform and Non Linear Approximation Coefficients. In: Huang, DS., Jo, KH., Lee, HH., Kang, HJ., Bevilacqua, V. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2009. Lecture Notes in Computer Science, vol 5754. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04070-2_35
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DOI: https://doi.org/10.1007/978-3-642-04070-2_35
Publisher Name: Springer, Berlin, Heidelberg
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