Iris Recognition Development Techniques: A Comprehensive Review

Jasem Rahman Malgheet,Lilly Suriani Affendey,Noridayu Bt Manshor,Rosa M Lopez Gutierrez

doi:10.1155/2021/6641247

Abstract

Recently, iris recognition techniques have achieved great performance in identification. Among authentication techniques, iris recognition systems have received attention very much due to their rich iris texture which gives robust standards for identifying individuals. Notwithstanding this, there are several challenges in unrestricted recognition environments. In this article, the researchers present the techniques used in different phases of the recognition system of the iris image. The researchers also reviewed the methods associated with each phase. The recognition system is divided into seven phases, namely, the acquisition phase in which the iris images are acquired, the preprocessing phase in which the quality of the iris image is improved, the segmentation phase in which the iris region is separated from the background of the image, the normalization phase in which the segmented iris region is shaped into a rectangle, the feature extraction phase in which the features of the iris region are extracted, the feature selection phase in which the unique features of the iris are selected using feature selection techniques, and finally the classification phase in which the iris images are classified. This article also explains the two approaches of iris recognition which are the traditional approach and the deep learning approach. In addition, the researchers discuss the advantages and disadvantages of previous techniques as well as the limitations and benefits of both the traditional and deep learning approaches of iris recognition. This study can be considered as an initial step towards a large-scale study about iris recognition.

Highlights

Computer vision is a significant research field, which provides efficient solutions to many problems
To discover eye images generated by the computer by exploring the differences in the eye region, Carvalho et al [97] introduced VGG architecture with 19 layers (VGG19) instead of the VGG architecture with 16 layers (VGG16) that was initially suggested by Simonyan and Zisserman [196] based on deep neural networks (DNNs) as the basis of the ImageNet
This paper has highlighted the use of hybrid techniques to enhance the performance efficiency of the iris recognition system by focusing on increasing accuracy and decreasing the computational complexity

Summary

Introduction

Computer vision is a significant research field, which provides efficient solutions to many problems. E security sector has given computer vision much attention, for identification. Modern security sciences use these unique features to control access to restricted places, which, in the field of security, is a fundamental problem. Traditional approaches to identification such as the use of a key or password are unsatisfactory in several application areas as these methods can be forgotten, stolen, or cracked. To overcome these weaknesses, modern science is interested in automating identification systems using biometric techniques [1]

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