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Performance Analysis of Canny, Sobel and Perwitt Edge Detection Methods in Biometric Security Identification
Current Issue
Volume 6, 2019
Issue 1 (February)
Pages: 5-10   |   Vol. 6, No. 1, February 2019   |   Follow on         
Paper in PDF Downloads: 31   Since Apr. 9, 2019 Views: 798   Since Apr. 9, 2019
Maskia Rahman, Electronics and Communication Engineering, Khulna University, Khulna, Bangladesh.
Md. Mehedi Hasan, Space Science Centre (ANGKASA), Universiti Kebangsaan Malaysia, Selangor, Malaysia.
Nowadays, biometric authentication is mostly used to protect access to highly confidential assets. Identity management is more important than ever as to strengthen global security, make transportation safer and protect a vital commercial entrance. Iris acknowledgment perceives individuals precisely and dependably in view of the irregular texture that noticeable on the iris of the eye while additionally being one of the minimum invasive. To build up an iris acknowledgment algorithm for individual distinguishing proof, this paper analyzes different edge recognition systems for various iris images. Algorithm needs to go through some basic images for pre-preparing steps because of iris image quality including nonlinearly twisted, separation, moving and faked iris images all are open issues in the iris acknowledgment framework. A fundamental work to take care of the problems to design and create algorithms for every one of these varieties of images. Albeit current literature has an assortment of edge detection methods like canny, Sobel, Prewitt and this paper does not always prompt satisfactory results. But the test result demonstrates that the Canny method has better capacity to distinguish point in the digital image, where image gray level changes even at a moderate rate. We have inspected noisy iris images applying salt and pepper noise as well as Gaussian noise. Different filtering techniques can be applied to eradicate the undesirable noise. The effects of edge detection techniques of the mean, median and Gaussian filtered images have been observed in the paper. By applying Gaussian filters at the vertical orientation is executed to normalized iris images and the time complexity of this methodology is lessened impressively. Experimental results demonstrate the legitimacy of this methodology.
Digital Image, Gaussian Noise, Image Edge Detection, Image Segmentation, Image Thresholding
M. Adhiyaman and D. Ezhilmaran, “Fingerprint Matching and Similarity Checking System using Minutiae Based Technique”, IEEE International Conference on Engineering and Technology, March 2015, India.
D. Peralta, I. Triguero, R. Sanchez-Reillo, F. Herrera, and J. M. Benítez, “Fast fingerprint identification for large databases,” Pattern Recognit., vol. 47, no. 2, 2014.
M. M. Hasan and M. F. Hossain, “Facial Features Detection in Color Images Based on Skin Color Segmentation”, IEEE 3rd International Conference on Informative, Electronics & Vision, 2014.
M. Price, J. Glass and A. P. Chandrakasan, “A 6 mW, 5,000-Word Real-Time Speech Recognizer Using WFST Models”, IEEE Journal of Solid-State Circuits, Jan-2015.
M. A. Ferrer, J. Fàbregas, M. Faundez, J. B. Alonso and C. Travieso, “Hand Geometry Identification System Performance”, IEEE 43rd Annual International Carnahan Conference on Security Technology, DOI: 10.1109/CCST.2009.5335545.
Chinese Academy of Sciences (CASIA) Iris Image Database (CASIA-Iris) 2010.
M. M. Hasan, J. M. Thakur and P. Podder, “Face Detection in Color Images Using Skin Color”, International Journal of Scientific & Engineering Research (ISSN 2229-5518), Volume 5, Issue 6, June-2014.
N. Barzegar, and M. S. Moin, “A new approach for iris localization in iris recognition systems”, IEEE/ACS International Conference on In Computer Systems and Applications, 2008.
M.. M. Hasan, M. F. Hossain, J. M. Thakur and P. Podder, “Driver Fatigue Recognition using Skin Color Modeling,” International Journal of Computer Applications, Volume: 97 (16), pp. 34-40, July 2014.
R. Ishikawa and T. Ohtsuka, “Pupil Center Detection by Abstracted Contour Graph Analysis for Iris Detection”, IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki, Japan, 2018.
A. Singh, M. Singh and B. Singh, “Face detection and eyes extraction using sobel edge detection and morphological operations”, Pune, India, 2016.
N. Jagadeesh; Chandrasekhar M. Patil, “An conceptual view of an iris-biometric identification system canny edge detection techniques”, Erode, India, 2017.
B. C Kovoor, M. H Supriya and K. P Jacob, “Iris biometric recognition system employing canny operator”, First International Conference On Computer Science & Information Technology (CS & IT), 2013.
U. T. Tania, S. M. A. Motakabber and M. I. Ibrahimy, “Edge Detection Techniques for Iris Recognition System”, IOP Conference Series. 2013.
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