Abstract
Pupil localization plays a key role in recognizing the biological characteristics of iris that is self-evident. In recent years, the most common procedures to localize the pupil have been based on the edge detector and circle finder, such as integro-differential operator and Hough transform. However, the circle finder overemphasizes geometric characteristics, which reduces the accuracy and real-time performance under complex conditions. In this chapter, a new threshold method using discrete gray gradient differentials based on the unique features of gaps in the gray gradients of pupil boundaries is proposed to binarization first. We then highlight the geometric characteristics of the pupil by adopting the strategy of combining outline filling with curve fitting to locate pupil boundaries. Compared to the Wildes system, the proposed method is feasible, fast, accurate, and stable.
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References
Ibrahim MT, Khan TM, Khan SA, Khan MA, Guan L. Iris localization using local histogram and other image statistics. Opt Lasers Eng. 2012;50:645–54.
Daugman JG. How iris recognition works. IEEE Trans Circuits Syst. 2004;14:21–30.
Daugman JG. High confidence visual recognition of persons by a test of statistical independence. IEEE Trans Pattern Anal Mach Intell. 1993;15:1148–61.
Bowyer KW, Hollingsworth K, Flynn PJ. Image understanding for iris biometrics: a survey. Comput Vis Image Understand. 2008;110:281–307.
Prewitt JMS, Mendelsohn ML. The analysis of cell images. Ann NY Acad Sci. 1966;128:1035–53.
Cui J, Wang Y, Tan T, Ma L, Sun Z. A fast and robust iris localization method based on texture segmentation. SPIE Def Secur Symp. 2004;5404:401–8.
Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern. 1979;1:62–6.
Wildes R. Iris recognition: an emerging biometric technology. Proc IEEE. 1997;85:1348–63.
Jan F, Usman I, Agha S. Reliable iris localization using Hough transform, histogram-bisection, and eccentricity. Signal Process. 2013;93:230–41.
Shah S, Ross A. Iris segmentation using geodesic active contours. IEEE Trans Inf Forensics Security. 2009;4:824–36.
Portilla J. Image denoising using scale mixtures of Gaussian in the wavelet domain. IEEE Trans Image Process. 2003;12:1338–51.
Gudbjartsson H, Patz S. The Rician distribution of noisy MRI data. Magn Reson Med. 1995;34:910–4.
Sauvola J, Pietikainen M. Adaptive document image binarization. Pattern Recogn. 2000;33:225–36.
Kakarala R. On achievable accuracy in edge localization. Proc IEEE Int Conf Acoust Speech Signal Process. 1991;4:2545–8.
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Lin, Y., Qu, Z., Zhang, Y., Han, H. (2015). A Fast and Accurate Pupil Localization Method Using Gray Gradient Differential and Curve Fitting. In: Wong, W. (eds) Proceedings of the 4th International Conference on Computer Engineering and Networks. Lecture Notes in Electrical Engineering, vol 355. Springer, Cham. https://doi.org/10.1007/978-3-319-11104-9_58
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DOI: https://doi.org/10.1007/978-3-319-11104-9_58
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11103-2
Online ISBN: 978-3-319-11104-9
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