Eye-Tracking Algorithm for Low Webcam Image Resolution Without Calibration
Resumo
Applications of eye-tracking devices aim to understand human activities and behaviors, improve human interactions with robots, and develop assistive technology in helping people with some communication disabilities. This paper proposes an algorithm to detect the pupil center and user’s gaze direction in real-time, using a low-resolution webcam and a conventional computer with no need for calibration. Given the constraints, the gaze space was reduced to five states: left, right, center, up, and eyes closed. A pre-existing landmarks detector was used to identify the user’s eyes. We employ image processing techniques to find the center of the pupil and we use the coordinates of the points found associated with mathematical calculations to classify the gaze direction. By using this method, the algorithm achieved 81.9% overall accuracy results even under variable and non-uniform environmental conditions. We also performed quantitative experiments with noise, blur, illumination, and rotation variation. Smart Eye Communicator, the proposed algorithm, can be used as eye-tracking mechanism to help people with communication difficulties to express their desires.
Referências
W. Chareonsuk, S. Kanhaun, K. Khawkam, and D. Wongsawang, "Face and Eyes mouse for ALS Patients," in 2016 Fifth ICT International Student Project Conference (ICT-ISPC). IEEE, may 2016, pp. 77–80. [Online]. Available: http://ieeexplore.ieee.org/document/7519240/
A. Lopez, I. Rodriguez, F. J. Ferrero, M. Valledor, and J. C. Campo, "Low-cost system based on electro-oculography for communication of disabled people," 2014 IEEE 11th International Multi-Conference on Systems, Signals and Devices, SSD 2014, pp. 1–6, 2014.
M. Arbesman and K. Sheard, "Systematic Review of the Effectiveness of Occupational Therapy-Related Interventions for People With Amyotrophic Lateral Sclerosis," American Journal of Occupational Therapy, vol. 68, no. 1, pp. 20–26, jan 2014. [Online]. Available: http://ajot.aota.org/Article.aspx?doi=10.5014/ajot.2014.008649
H. M. Elahi, D. Islam, I. Ahmed, S. Kobashi, and M. A. R. Ahad, "Webcam-based accurate eye-central localization," Proceedings - 2013 2nd International Conference on Robot, Vision and Signal Processing, RVSP 2013, pp. 47–50, 2013.
J. R. Khonglah and A. Khosla, "A low cost webcam based eye tracker for communicating through the eyes of young children with ASD," Proceedings on 2015 1st International Conference on Next Generation Computing Technologies, NGCT 2015, no. September, pp. 925–928, 2016.
R. Kaushik, T. Arora, Sukanya, and R. Tripathi, "Design of Eyewriter for ALS Patients through Eyecan," in 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN). [Online]. Available: https://ieeexplore.ieee.org/document/8748520/ IEEE, oct 2018, pp. 991–995.
J. Cech and T. Soukupova, "Real-Time Eye Blink Detection using Facial Landmarks," Center for Machine Perception, Department of Cybernetics Faculty of Electrical Engineering, Czech Technical University in Prague, pp. 1 – 8, 2016.
V. Kazemi and J. Sullivan, "One millisecond face alignment with an ensemble of regression trees," in CVPR, 2014.
