A Qualitative Performance Comparison Of Supervised Machine Learning Algorithms For Iris Recognition
European Journal of Molecular & Clinical Medicine,
2020, Volume 7, Issue 6, Pages 1937-1946
Abstract
Iris Scanning is the process of biometric authentication that uses classification techniques. Safety is the main issue for humans nowadays. Biometric authentication provides more security because of its uniqueness. Iris recognition is the process of identifying individuals using iris patterns. This study aims to develop a modal for iris recognition using Machine Learning methods. The main goal is to progress iris image acquisition, segmentation, classification, Texture Analysis, Feature extraction, cross-sensor recognition, and pattern matching for biometric authentication or verification. This paper reviews a background of iris recognition and literature of recently proposed machine learning methods in different fields of the iris recognition system. The core ideas of various methods and their relationships are investigated to obtain an overview and insights into the development of iris recognition.
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