Pupil Detection System Implementation for Low-Resolution Eye Images Based on a Fully-Connected Neural Network Classifier

Gabriel Bonteanu; Petronela Bonteanu; Nicolae Patache; Arcadie Cracan; Radu Gabriel Bozomitu

doi:10.2478/bipie-2024-0008

Abstract

This paper presents an implementation of a pupil center detection system that uses a classifier based on neural networks. Being intended for the processing of low-resolution eye images, the implementation is done with a fully connected neural network. The classes of the classifier are defined according to the possible positions of the pupil center. The neural network was trained, tested and validated using 39,000 eye images from 18 databases, images obtained from different human subjects and under variable and non-uniform lighting conditions. The solution is distinguished by the fact that it directly provides the coordinates of the center of the pupil and not just the area where it is located. The results show both high accuracy and high processing speed, the proposed solution being suitable for low-budget implementations of real-time gaze detection systems.

References

Bojarski M., Del Testa D., Dworakowski D., Firner B., Flepp B., Goyal P., Jackel L.D., Monfort M., Muller U., Zhang J., Zhang X., End to End Learning for Self-Driving Cars, In Proceedings of the International Conference on Robotics and Auto-mation, 15(4), 2016; pp. 123-137, doi.org/10.48550/arXiv.1604.07316.
Search in Google Scholar Back to article
Bonteanu G., Bonteanu P., Cracan A., Bozomitu R.G., Implementation of a High-Accuracy Neural Network-Based Pupil Detection System for Real-Time and Real-World Applications, Sensors 2024, 24, 2548, https://doi.org/10.3390/s24082548.
Search in Google Scholar Back to article
Bozomitu R.G., Păsărică A., Tărniceriu D., Rotariu C., Development of an Eye Tracking-Based Human-Computer Interface for Real-Time Applications, Sensors 2019, Vol. 19, Issue 16, Article Number 3630.
Search in Google Scholar Back to article
Cherabit N., Djeradi A., Chelali F.Z., Circular Hough transform for iris localization, Science and Technology 2012, vol. 2, no. 5, pp. 114-121.
Search in Google Scholar Back to article
Dampage U., Balasuriya C., Thilakarathna S., Rathnayaka D., Kalubowila L., AI-Based Heart Monitoring System, In Proceedings of the IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON), Kuala Lumpur, Malaysia, 2021, pp. 1-6, 10.1109/GUCON50781.2021.9573888.
Search in Google Scholar Back to article
De Coster M., Van Herreweghe M., Dambre J., Sign language recognition with transformer networks, In Proceedings of the 12th international conference on language resources and evaluation (LREC 2020), 2020, pp. 6018-6024.
Search in Google Scholar Back to article
Farshad M., Karimi M., A Signal Segmentation Approach to Identify Incident/Reflected Traveling Waves for Fault Location in Half-Bridge MMC-HVdc Grids, IEEE Transactions on Instrumentation and Measurement 2021, vol. 71, pp. 1-9, 10.1109/TIM.2021.3139688.
Search in Google Scholar Back to article
Fitzgibbon A.W., Pilu M., Fisher R.B., Direct least squares fitting of ellipses, In Proceedings of 13th International Conference on Pattern Recognition, Vienna, Austria, 25-29 August 1996, Volume 1, pp. 253-257.
Search in Google Scholar Back to article
Fuhl W., Kübler T., Sippel K., Rosenstiel W., Kasneci E., Excuse: Robust pupil detection in real-world scenarios, In Proceedings of the 16th International Conference on Computer Analysis of Images and Patterns, Valletta, Malta, 2–4 September 2015, pp. 39-51.
Search in Google Scholar Back to article
Fuhl W., Santini T., Kasneci G., Rosenstiel W., Kasneci E., PupilNet v2.0: Convolutional Neural Networks for CPU based real time Robust Pupil Detection, arXiv preprint arXiv:1711.00112, 2017.
Search in Google Scholar Back to article
Fuhl W., Santini T.C., Kübler T., Kasneci E., Else: Ellipse selection for robust pupil detection in real-world environments, In Proceedings of the 9th Biennial ACM Symposium on Eye Tracking Research & Applications, Charleston, SC, USA, 14–17 March 2016, pp. 123-130.
Search in Google Scholar Back to article
Klaib A., Alsrehin N., Melhem W., Bashtawi H., Magableh A., Eye tracking algorithms, techniques, tools, and applications with an emphasis on machine learning and Internet of Things technologies, Expert Systems with Applications 2021, Vol. 166, doi.org/10.1016/j.eswa.2020.114037.
Search in Google Scholar Back to article
Kocejko T., Bujnowski A., Wtorek J., Eye mouse for disabled, In 2008 Conference on Human System Interactions, 2008, pp. 199-202.
Search in Google Scholar Back to article
Krizhevsky A., Sutskever I., Hinton G.E., ImageNet Classification with Deep Convolutional Neural Networks, Communica-tions of the ACM 2017, Vol. 60, no. 6, pp. 84-90, doi.org/10.1145/3065386.
Search in Google Scholar Back to article
Li D., Parkhurst J.D., Starburst: A robust algorithm for video-based eye tracking, In Proceedings of the IEEE Vision for Hu-man-Computer Interaction Workshop, Iowa State University, Ames, IA, USA, 5 September 2005.
Search in Google Scholar Back to article
Liu B., Sentiment Analysis and Opinion Mining, Springer Cham, Springer Nature Switzerland AG 2012, 2022, doi.org/10.1007/978-3-031-02145-9.
Search in Google Scholar Back to article
Santini T., Fuhl W., Kasneci E., PuReST: Robust pupil tracking for real-time pervasive eye tracking. In Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications, Warsaw, Poland, 14–17 June 2018, p. 61.
Search in Google Scholar Back to article
Swirski L., Bulling A., Dodgson N.A., Robust Real-Time Pupil Tracking in Highly off-Axis Images, In Proceedings of the Symposium on Eye Tracking Research and Applications, Santa Barbara, CA, USA, 28–30 March 2012, pp. 173-176.
Search in Google Scholar Back to article
Thennakoon A., Bhagyani C., Premadasa S., Mihiranga S., Kuruwitaarachchi N., Real-time Credit Card Fraud Detection Using Machine Learning, In Proceedings of the 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 2019, pp. 488-493, 10.1109/CONFLUENCE.2019.8776942.
Search in Google Scholar Back to article
Wang Q., Yang J., Eye detection in facial images with unconstrained background, Journal of pattern recognition research, 1(1), 2006, 55-62.
Search in Google Scholar Back to article
Xiong J., Zhang Z., Wang C., Cen J., Wang Q., Nie J., Pupil localization algorithm based on lightweight convolutional neural network, The Visual Computer 2024, pp. 1-17.
Search in Google Scholar Back to article
Xu G., Zhang Z., Ma Y., A Novel and Efficient Method for Iris Automatic Location, Journal of China University of Mining and Technology. Volume 17, Issue 3, 2007, pp. 441-446, doi.org/10.1016/S1006-1266(07)60122-9.
Search in Google Scholar Back to article
Zhou Z.-H., Geng X., Projection functions for eye detection, Pattern recognition 2004, vol. 37, no. 5; pp. 1049-1056.
Search in Google Scholar Back to article

Pupil Detection System Implementation for Low-Resolution Eye Images Based on a Fully-Connected Neural Network Classifier

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