Towards Accurate Pupil Detection Based on Morphology and Hough Transform

Ebtesam Alshemmary Et Al

doi:10.21123/bsj.2020.17.2.0583

Abstract

Automatic recognition of individuals is very important in modern eras. Biometric techniques have emerged as an answer to the matter of automatic individual recognition. This paper tends to give a technique to detect pupil which is a mixture of easy morphological operations and Hough Transform (HT) is presented in this paper. The circular area of the eye and pupil is divided by the morphological filter as well as the Hough Transform (HT) where the local Iris area has been converted into a rectangular block for the purpose of calculating inconsistencies in the image. This method is implemented and tested on the Chinese Academy of Sciences (CASIA V4) iris image database 249 person and the IIT Delhi (IITD) iris database v1 using MATLAB 2017a. This method has high accuracy in the center and radius finding reaches 97% for 2268 iris on CASIA V4 image and 99.77% for 2240 iris images on IITD, the speed is acceptable compared to the real-time detection speed and stable performance.

Highlights

Security is one of the most important topics which aims to protect human rights from thieves
Experimental Results the implementation of the proposed method is analyzed on CASIA V4database consisting of 384 different subjects and IIT Delhi (IITD) iris image database
The introduced method relies on morphology operation and Hough Transform (HT) to select the center of pupil and radius, and calculate the radius of the iris

Summary

Introduction

Security is one of the most important topics which aims to protect human rights from thieves. Wildes [6] applied a Hough Transform (HT) to determine the iris boundaries. Both algorithms are powerful in the performance but they are costly to calculate the fullest image. Hough transformation for lines does not apply the above illustration for lines, as the lines perpendicular to the coordinate axis will have an infinity a-value. This can force the infinite size of the parameter space a, b.

Proposed Methodologies

Begin Algorithm

End Algorithm

Proposed Morphology and Hough

Conclusion