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

S. Navaneethan1,*, 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: email

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

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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APA Style
Navaneethan, S., Sreedhar, P.S.S., Padmakala, S., Senthilkumar, C. (2023). The human eye pupil detection system using BAT optimized deep learning architecture. Computer Systems Science and Engineering, 46(1), 125-135. https://doi.org/10.32604/csse.2023.034546
Vancouver Style
Navaneethan S, Sreedhar PSS, Padmakala S, Senthilkumar C. The human eye pupil detection system using BAT optimized deep learning architecture. Comput Syst Sci Eng. 2023;46(1):125-135 https://doi.org/10.32604/csse.2023.034546
IEEE Style
S. Navaneethan, P.S.S. Sreedhar, S. Padmakala, and C. Senthilkumar "The Human Eye Pupil Detection System Using BAT Optimized Deep Learning Architecture," Comput. Syst. Sci. Eng., vol. 46, no. 1, pp. 125-135. 2023. https://doi.org/10.32604/csse.2023.034546



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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