Open Access

ARTICLE

The Human Eye Pupil Detection System Using BAT Optimized Deep Learning Architecture

S. Navaneethan^1,*, P. Siva Satya Sreedhar², S. Padmakala³, C. Senthilkumar⁴

1 Department of Electronics and Communication Engineering, Saveetha Engineering College, Chennai, 602105, India
2 Department of Information Technology, Seshadri Rao Gudlavalleru Engineering College, Krishna, 521356, India
3 Department of Computer Science Engineering, Saveetha School of Engineering, SIMATS, Chennai, 602105, India
4 Department of Electronics and Communication Engineering, Saveetha School of Engineering, SIMATS, Chennai, 602105, India

* Corresponding Author: S. Navaneethan. Email:

Computer Systems Science and Engineering 2023, 46(1), 125-135. https://doi.org/10.32604/csse.2023.034546

Received 20 July 2022; Accepted 29 September 2022; Issue published 20 January 2023

Abstract

The pupil recognition method is helpful in many real-time systems, including ophthalmology testing devices, wheelchair assistance, and so on. The pupil detection system is a very difficult process in a wide range of datasets due to problems caused by varying pupil size, occlusion of eyelids, and eyelashes. Deep Convolutional Neural Networks (DCNN) are being used in pupil recognition systems and have shown promising results in terms of accuracy. To improve accuracy and cope with larger datasets, this research work proposes BOC (BAT Optimized CNN)-IrisNet, which consists of optimizing input weights and hidden layers of DCNN using the evolutionary BAT algorithm to efficiently find the human eye pupil region. The proposed method is based on very deep architecture and many tricks from recently developed popular CNNs. Experiment results show that the BOC-IrisNet proposal can efficiently model iris microstructures and provides a stable discriminating iris representation that is lightweight, easy to implement, and of cutting-edge accuracy. Finally, the region-based black box method for determining pupil center coordinates was introduced. The proposed architecture was tested using various IRIS databases, including the CASIA (Chinese academy of the scientific research institute of automation) Iris V4 dataset, which has 99.5% sensitivity and 99.75% accuracy, and the IIT (Indian Institute of Technology) Delhi dataset, which has 99.35% specificity and MMU (Multimedia University) 99.45% accuracy, which is higher than the existing architectures.

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Keywords

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