Abstract
Iris segmentation is an essential precondition for biometric authentication systems based on iris recognition and dramatically affects the accuracy of personal identification. Due to various noises during iris acquisition, iris images from different databases exhibit different texture characteristics. Existing works mostly design segmentation schemes for specific iris images and thus restrain much room for performance improvement. Therefore, this paper proposes a race classification based iris image segmentation method. Compared with conventional methods, the proposed method firstly exploits the merits of local Gabor binary pattern (LGBP) with support vector machine (SVM) and builds an efficient classifier, LGBP-SVM, to partition iris images into the human eye and non-human eye images. Following this, these two kinds of iris images are segmented by different strategies based on circular Hough transform with the active contour model. Extensive experiments demonstrate the proposed LGBP-SVM outperforms existing works in terms of accuracy of iris race classification. Furthermore, the race classification based iris segmentation method improves the segmentation accuracy and correct segmentation rates for various iris image databases.
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Acknowledgments
This work is supported by the Key Program of NSFC-Tongyong Union Foundation (Grant No. U1636209), the National Natural Science Foundation of China (Grant No. 61902292), the Key Research and Development Programs of Shaanxi (Grant Nos. 2019ZDLGY13-07 and 2019ZDLGY13-04), and the Science and Technology Projects of Xi’an, China (Grant No. 201809170CX11JC12).
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Ke, X., An, L., Pei, Q., Wang, X. (2020). Race Classification Based Iris Image Segmentation. In: McDaniel, T., Berretti, S., Curcio, I., Basu, A. (eds) Smart Multimedia. ICSM 2019. Lecture Notes in Computer Science(), vol 12015. Springer, Cham. https://doi.org/10.1007/978-3-030-54407-2_32
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DOI: https://doi.org/10.1007/978-3-030-54407-2_32
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