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

Computer Systems Science and Engineering, Vol.46, No.1, pp. 125-135, 2023, DOI: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… More >

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

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 33-50, 2022, DOI:10.32604/cmc.2022.028190

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… More >