Eye Blinking for Command Generation Based on Deep Learning
Due to progress in the field of deep learning in order to find and track objects through the use of computer vision in the service of large segments of the population, as it was adopted in the field of serving people b with special needs for the sake of dialogue and implementation of many requests in this research, a series of commands for use by people with special needs with speech problems or paralysis, where the ability to use the eye blink is very useful for social communication, were developed. In this research, the orders needed by the target people with special needs were studied, and (11) commands were identified that can be increased according to the intended sample. A table of commands was built depending on the length of the eye blink period. By creating a modified CNN: Convolutional Neural Network structure and training it on 2 different database, deep learning was used to identify and determine if the eye is closed or open (mrlEye2018 and Closed Eye in the Wlid:CEW).It was then followed by a test on all of the chosen examples, in different contexts and at various ages. The test on the data yielded excellent results, with 99% percent accuracy on data from test samples and 97.5% and 96% percent accuracy on training data in each case. To check the cases (11) suggested commands, the proposed system was evaluated on a collection of videos taken in real time and under normal recording circumstances through a camera, and the correctness of generating the codes that was proposed was 94% percent on four tries in each of the above instances.
Ekim G, Atasoy A, İkizler N. A. ,”New Approach for Eye-Blink to Speech Conversion by Dynamic Time Warping”, Traitement du Signal. 2021 Apr 1;38(2).
Anas ER, Henriquez P, Matuszewski BJ. ,”Online eye status detection in the wild with convolutional neural Networks”, In International Conference on Computer Vision Theory and Applications 2017 Feb 27 (Vol. 7, pp. 88- 95). SciTePress.
Pothuraju Vishesh , Raghavendra S, Santosh Kumar Jankatti, Rekha V. ,”Eye blink detection using CNN to detect drowsiness level in drivers for road safety” , Indonesian Journal of Electrical Engineering and Computer Science Vol. 22, No. 1, April 2021, pp. 222-231
Kumar K, Srikar VS, Swapnika Y, Sravani VS, Aditya N. ,”A novel approach for Morse code detection from eye blinks and decoding using OpenCV”, International Journal for Research in Applied Science & Engineering Technology (IJRASET). 2020;8.
Rahman MM, Islam MS, Jannat MK, Rahman MH, Arifuzzaman M, Sassi R, Aktaruzzaman M. ,” EyeNet: An improved eye states classification system using convolutional neural network”, In 2020 International Conference on Advanced Communication Technology (ICACT) 2020 Feb 16 (pp. 84-90). IEEE.
Ali, A., RASHEED, M., SHIHAB, S., RASHID, T., Sabri, A., & Abed Hamed, S. (2021). An Effective Color Image
Detecting Method for Colorful and Physical Images. Journal of Al-Qadisiyah for Computer Science and
Mathematics, 13(1), Comp Page 88 -98. https://doi.org/10.29304/jqcm.2021.13.1.778.
Abdulelah, A., Abed Hamed, S., RASHEED, M., SHIHAB, S., RASHID, T., & Kamil Alkhazraji, M. (2021). The
Application of Color Image Compression Based on Discrete Wavelet Transform. Journal of Al-Qadisiyah for
Computer Science and Mathematics, 13(1), Comp Page 18 -25. https://doi.org/10.29304/jqcm.2021.13.1.762
Mustafa Siddeq, M., & Abdullah Anwar, D. (2017). Using Perceptron Neural Network and Genetic Algorithm for
Image Compression and Decompression. Journal of Al-Qadisiyah for Computer Science and Mathematics, 3(1),
-296. Retrieved from https://qu.edu.iq/journalcm/index.php/journalcm/article/view/261
Turky, S., Ahmed AL-Jumaili, A., & Hasoun, R. (2021). Deep Learning Based On Different Methods For Text
Summary: A Survey. Journal of Al-Qadisiyah for Computer Science and Mathematics, 13(1), Comp Page 26-35.
G. E. Hinton, S. Osindero, and Y.-W. Teh, “A fast learning algorithm for deep belief nets,” Neural computation, vol. 18, pp. 1527–1554, 2006.
P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol, “Extracting and composing robust features with denoising autoencoders,” in International Conference on Machine learning, 2008, pp. 1096–1103.
Y. LeCun, L. Bottou, Y. Bengio, P. Haffner et al., “Gradient-based learning applied to document recognition”, Proceedings of the IEEE, vol. 86, pp. 2278–2324, 1998.
Udayashankar A, Kowshik AR, Chandramouli S, Prashanth HS.,” Assistance for the paralyzed using eye blink detection”, In 2012 Fourth International Conference on Digital Home 2012 Nov 23 (pp. 104-108). IEEE.
Juneja K, Rana C.,” Structural and Statistical Similarity Measure based Approach for Effective Eye Blink Recognition”, Pertanika Journal of Science & Technology. 2019 Apr 1;27(2).
Jordan AA, Pegatoquet A, Castagnetti A, Raybaut J, Le Coz P. ,” Deep Learning for Eye Blink Detection Implemented at the Edge”, IEEE Embedded Systems Letters. 2020 Oct 7.
Baccour MH, Driewer F, Kasneci E, Rosenstiel W.,” Camera-based eye blink detection algorithm for assessing driver drowsiness”, In2019 IEEE Intelligent Vehicles Symposium (IV) 2019 Jun 9 (pp. 987-993). IEEE.
Sharmila TS, Srinivasan R, Nagarajan KK, Athithya S. ,”Eye Blink Detection Using Back Ground Subtraction and Gradient-Based Corner Detection for Preventing CVS”, Procedia Computer Science. 2019 Jan 1;165:781-9.
Jarah, N. (2021). Deep Learning In Wireless Sensor Network. Journal of Al-Qadisiyah for Computer Science and
Mathematics, 13(1), Comp Page 11 -17. https://doi.org/10.29304/jqcm.2021.13.1.755.
Deshpande SN, Deshmukh VA, Arjun GD, Goskonda HR, Butala AR, Datar DS.” Human Computer Interaction through Morse Code”, International Journal of Research in Engineering and Science (IJRES) .vol. 9 ,Issue 7, pp. 54-61
Tran C, Namazi N. ,”Real-time Detection of Early Drowsiness Using Convolution Neural Networks”, Electronic Imaging. 2021 Jan 18;2021(8):233-1.