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An Improved Biometric Authentication with Scleral Patterns

T. Priyadharshini


Biometrics is the science of recognizing people based on their physical or behavioral traits such as face, fingerprints, iris, and voice. Iris is unique part of the eye that is varied from person to person. Iris is white part of the eye and it covering the sclera. The accuracy of the iris recognition system depends on the image quality of the iris images and also iris recognition requires frontal gaze images of the eye. Single biometric system shows certain disadvantages, non-universality, spoof attacks etc. The sclera is the white section of the eye, is the opaque, rubbery, defensive, external layer of the eye containing collagen and elastic fiber images of sclera vessel patterns are often defocused and saturated and most significantly, the vessel structure in the sclera is multilayered and has complex nonlinear transformations, sclera recognition system is not given sufficient accuracy. This article investigates the use of scleral texture in the sclera as a potential biometric. Iris patterns are better discerned in the near-infrared spectrum (NIR) while vascular patterns are better discerned in the visible spectrum (RGB). Most of the results proved that Intelligent Recognition System using Iris and Sclera Features developed is more accurate and efficient as compared to other single biometric systems.

Cite this Article
T. Priyadharshini. An Improved Biometric Authentication with Scleral Patterns. Journal of Advances in Shell Programming. 2016; 3(1): 28–35p.


Biometrics, sclera, iris recognition, pattern recognition, conjunctival vasculature

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Flom SL, Safir A. Iris recognition system. U.S. Patent 4 641 349. 1987.

Daugman JG. High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell. 1993; 15(11): 1148–1161p.

Wildes RP. Iris recognition: An emerging biometric technology. Proc. IEEE. 1997; 85(9): 1348–1363p.

Boles WW Boashash B. A human identification technique using images of the iris and wavelet transform. IEEE Trans. Signal Process. 1998; 46(4): 1185–1188p.

Ma L et al. Efficient iris recognition by characterizing key local variations. IEEE Trans. Image Process. 2004; 13(6): 739–750p.

Vatsa M, Singh R, Noore A. Improving iris recognition performance using segmentation, quality enhancement, match score fusion, and indexing. IEEE Trans. Syst., Man, Cybern. B, Cybern. 2008; 38(4): 1021–1035p.

Derakhshani R, Ross A, Crihalmeanu S. A New Biometric Modality Based on Conjunctival Vasculature. Proc. of Artificial Neural Networks in Engineering. 2006.

Derakhshani R. Ross A. A Texture-Based Neural Network Classifier for Biometric Identification using Surface Vasculature. in Proc. of the International Joint Conference on Neural Networks, Orlando, FL. 2007:2982–2987p.

Crihalmeanu S, Ross A, Derakhshani R. Enhancement and Registration Schemes for Matching Conjunctival Vasculature. in Proceedings of the Third International Conference on Advances in Biometrics Alghero, Italy: SpringerVerlag, 2009.

Stanton AD, Tom AV, Bharath SA, Parker AA. Segmentation of retinal blood vessels based on the second directional derivative and region growing. In: Internat. Conf. on Image Processing (ICIP). 1999; 2: 173–176p.


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