Surf and Sift Descriptors Using Wavelet Transforms for Iris Recognition

Kamal Majeed, Mohammed (2020) Surf and Sift Descriptors Using Wavelet Transforms for Iris Recognition. Surf and Sift Descriptors Using Wavelet Transforms for Iris Recognition, 13 (4). 2361--2373.

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Abstract

Iris recognition is a well-known accurate biometric technology and major research area in pattern recognition and computer vision available today. It targets human recognition through the person’s iris recognition without human intervention. In many areas iris recognition plays well such as bioinformatics, machine vision, pattern recognition, etc., and it is one of the popular subjects still. Finding of features to identify an iris, which is a small black part of an eye, is a difficult problem in iris recognition. Many methods and algorithms have been proposed on feature extraction, which include aspects like statistical features, level of invariance and robustness. In this article, a traditional SURF and SIFT algorithms are tested for iris recognition. To improve the performance of these algorithms, we passed the input through different domains from the real time. Through applying the Gabor Wavelet Transform (GWT) or Discrete Wavelet Transform (DWT)to the input iris images, a denser and more clear images obtained compared to those by the traditional SURF and SIFT. Thus the simulations of the proposed approaches of using Gabor Wavelet Transform or Discrete Wavelet Transform on SURF and SIFT algorithms gives better results compared to the traditional algorithms.

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