Open Access

ARTICLE

Robust and High Accuracy Algorithm for Detection of Pupil Images

Waleed El Nahal¹, Hatim G. Zaini², Raghad H. Zaini³, Sherif S. M. Ghoneim^4,*, Ashraf Mohamed Ali Hassan⁵

1 Electronics and Communications Engineering Department, Faculty of Engineering, MSA University, CO, 12585, Egypt
2 Computer Engineering Department, College of Computer and Information Technology, Taif University, Taif, 21944, Saudi Arabia
3 Faculty of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
4 Department of Electrical Engineering, College of Engineering, Taif University, Taif, 21944, Saudi Arabia
5 Electronics and Communications Engineering Department, Faculty of Engineering, Sinai University, Arish, CO, 45511, Egypt

* Corresponding Author: Sherif S. M. Ghoneim. Email:

Computers, Materials & Continua 2022, 73(1), 33-50. https://doi.org/10.32604/cmc.2022.028190

Received 04 February 2022; Accepted 21 March 2022; Issue published 18 May 2022

Abstract

Recently, many researchers have tried to develop a robust, fast, and accurate algorithm. This algorithm is for eye-tracking and detecting pupil position in many applications such as head-mounted eye tracking, gaze-based human-computer interaction, medical applications (such as deaf and diabetes patients), and attention analysis. Many real-world conditions challenge the eye appearance, such as illumination, reflections, and occasions. On the other hand, individual differences in eye physiology and other sources of noise, such as contact lenses or make-up. The present work introduces a robust pupil detection algorithm with and higher accuracy than the previous attempts for real-time analytics applications. The proposed circular hough transform with morphing canny edge detection for Pupillometery (CHMCEP) algorithm can detect even the blurred or noisy images by using different filtering methods in the pre-processing or start phase to remove the blur and noise and finally the second filtering process before the circular Hough transform for the center fitting to make sure better accuracy. The performance of the proposed CHMCEP algorithm was tested against recent pupil detection methods. Simulations and results show that the proposed CHMCEP algorithm achieved detection rates of 87.11, 78.54, 58, and 78 according to Świrski, ExCuSe, Else, and labeled pupils in the wild (LPW) data sets, respectively. These results show that the proposed approach performs better than the other pupil detection methods by a large margin by providing exact and robust pupil positions on challenging ordinary eye pictures.

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Keywords

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