Performance Analysis of Canny, Sobel and Perwitt Edge Detection Methods in Biometric Security Identification
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
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