Pengenalan Iris menggunakan Ekstraksi Fitur Histogram of Oriented Gradient

Siska Devella


Iris is a biometric-based on physiological characteristics. Iris is completely unique, the patterns of one person's two eyes are quite different from each other and even genetically identical twins have different iris patterns. The Iris of a person is stable throughout a person's life. Therefore, the iris recognition system has a high level of security. This study proposed iris recognition system using Histogram of Oriented Gradient (HOG) as feature extraction and two classifier K - Nearest Neighbors (K-NN) and Naive Bayes. CASIA Iris Interval V4.0 database is utilized to evaluate the performance of the proposed methods. The experimental results show that the iris image with normalization has a better accuracy when compared with iris images without normalization. The highest accuracy in this research is HOG + KNN for iris with normalization, with accuracy 96%.


L. Masek, “Recognition of Human Iris Patterns for Biometrics Identification,” University of Western Australia, 2003.

R. Malutan, S. Emerich, and O. Pop, “Half Iris Biometrie System Based on HOG and LIOP A . Histogram ofOriented Gradients ( HOG ) Loeal lntensity Order Pattern ( LlOP ),” 2016 2nd Int. Conf. Front. Signal Process., pp. 99–103, 2016.

S. B. Kulkarni, R. B. Kulkarni, U. P. Kulkarni, and R. S. Hegadi, “GLCM-Based Multiclass Iris Recognition Using FKNN and KNN,” Int. J. Image Graph., vol. 14, no. 3, pp. 1450010–1–1450010–27, 2014.

G. Savithiri and A. A.Murugan, “Performance Analysis on Half Iris Feature Extraction using GW, LBP and HOG,” Int. J. Comput. Appl., vol. 22, no. 2, pp. 27–32, 2011.

J. G. Daugman, “Biometric personal identification system based on iris analysis,” 1994.

A. Radman, N. Zainal, and S. A. Suandi, “Automated segmentation of iris images acquired in an unconstrained environment using HOG-SVM and GrowCut,” Digit. Signal Process. A Rev. J., vol. 64, pp. 60–70, 2017.

A. Radman, N. Zainal, and K. Jumari, “Fast and reliable iris segmentation algorithm,” IET Image Process., vol. 7, no. 1, pp. 42–49, 2013.

S. Agarwal and R. K. Nayak, “Comparision of Iris Identification by Using modified SIFT and SURF keypoint Descriptor Comparision of Iris Identification by Using modified SIFT and SURF keypoint Descriptor,” National Institute of Technology Rourkela, 2013.

C. A. of S. Institute of Automation, “Biometrics Ideal Test,” Center for Biometrics and Security Research. [Online]. Available:

L. Flom and A. Safir, “Iris recognition system,” 1987.

S. Prabhakar, S. Pankanti, and A. K. Jain, “Biometric recognition: security and privacy concerns,” IEEE Secur. Priv. Mag., vol. 1, no. 2, pp. 33–42, 2003.

M. Alhamrouni, “Iris Recognition By Using Image Processing Techniques,” Atilim University, 2017.

N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” Proc. - 2005 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognition, CVPR 2005, vol. I, pp. 886–893, 2005.

S. G. Qureshi, “Designing and Implementation of Iris recognition System Using Morphological Bridged Canny Edge Detection and KNN Classifier,” vol. 4, no. 6, pp. 12604–12609, 2015.

M. Erbilek, M. C. Da Costa-Abreu, and M. Fairhurst, “Optimal configuration strategies for iris recognition processing,” IET Conf. Image Process. (IPR 2012), pp. B2–B2, 2012.

H. Mehrotra, B. Majhi, and P. Gupta, “Annular Iris Recognition Using SURF,” pp. 464–469, 2009.

N. S. Sarode and A. M. Patil, “Iris Recognition using LBP with Classifiers-KNN and NB,” vol. 4, no. 1, pp. 1904–1908, 2015.

C. Li, W. Zhou, and S. Yuan, “Iris recognition based on a novel variation of local binary pattern,” Vis. Comput., vol. 31, no. 10, pp. 1419–1429, 2015.



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