Research Article
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Year 2022, Volume: 3 Issue: 2, 65 - 69, 28.12.2022

Kenan Donuk Davut Hanbay

https://doi.org/10.55195/jscai.1216384

Abstract

References

  • M. Yu et al., “An easy iris center detection method for eye gaze tracking system,” J Eye Mov Res, vol. 8, no. 3, 2015, doi: 10.16910/JEMR.8.3.5.
  • K. Donuk and D. Hanbay, “Video Based Real-Time Eye Tracking,” in In: 28th IEEE Signal Processing and Communications Applications (SIU-2020), 2020, pp. 21–24.
  • Q. Zhuang, Z. Kehua, J. Wang, and Q. Chen, “Driver fatigue detection method based on eye states with pupil and iris segmentation,” IEEE Access, vol. 8, pp. 173440–173449, 2020, doi: 10.1109/ACCESS.2020.3025818.
  • H. Yan and Y. Zhang, “Detection of the pupil eigenvalues in medicine,” Proceedings - 2010 International Conference on Computational and Information Sciences, ICCIS 2010, pp. 989–992, 2010, doi: 10.1109/ICCIS.2010.244.
  • “dlib C++ Library.” http://dlib.net/ (accessed Dec. 08, 2022).
  • V. Kazemi and J. Sullivan, “One millisecond face alignment with an ensemble of regression trees,” in 2014 IEEE Conference on Computer Vision and Pattern Recognition, Jun. 2014, pp. 1867–1874. doi: 10.1109/CVPR.2014.241.
  • K. Zhang, Z. Zhang, Z. Li, and Y. Qiao, “Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks,” IEEE Signal Process Lett, vol. 23, no. 10, pp. 1499–1503, Oct. 2016, doi: 10.1109/LSP.2016.2603342.
  • “Home - OpenCV.” https://opencv.org/ (accessed Dec. 08, 2022).
  • K. Donuk and D. Hanbay, “Pupil Center Localization Based on Mini U-Net,” Computer Science, pp. 185–191, Oct. 2022, doi: 10.53070/BBD.1173482.
  • K. il Lee, J. H. Jeon, and B. C. Song, “Deep Learning-Based Pupil Center Detection for Fast and Accurate Eye Tracking System,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12364 LNCS, pp. 36–52, 2020, doi: 10.1007/978-3-030-58529-7_3/COVER.
  • A. Villanueva, V. Ponz, L. Sesma-Sanchez, M. Ariz, S. Porta, and R. Cabeza, “Hybrid method based on topography for robust detection of iris center and eye corners,” ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), vol. 9, no. 4, Aug. 2013, doi: 10.1145/2501643.2501647.
  • O. Jesorsky, K. J. Kirchberg, and R. W. Frischholz, “Robust Face Detection Using the Hausdorff Distance,” 2001, pp. 90–95. doi: 10.1007/3-540-45344-X_14.
  • A. Larumbe-Bergera, G. Garde, S. Porta, R. Cabeza, and A. Villanueva, “Accurate Pupil Center Detection in Off-the-Shelf Eye Tracking Systems Using Convolutional Neural Networks,” Sensors, vol. 21, no. 20, p. 6847, Oct. 2021, doi: 10.3390/s21206847.

Real-time Iris Center Detection Based on Convolutional Neural Networks

Year 2022, Volume: 3 Issue: 2, 65 - 69, 28.12.2022

Kenan Donuk Davut Hanbay

https://doi.org/10.55195/jscai.1216384

Abstract

It is an active field of study in studies where the iris center is referenced, such as iris center detection, gaze tracking, driver fatigue detection. In this study, an approach for real-time detection of iris centers based on convolutional neural networks is presented. The GI4E dataset was used as the dataset for the proposed approach. Experimental results estimated the test data of the proposed convolutional neural network model with an accuracy of 97.2% based on the 0.025 error corresponding to the closest position to the iris center according to the maximum normalized error criteria. The study was also tested in real time with a webcam built into the computer. While the test accuracy is satisfactory, real-time speed performance needs to be improved.

Keywords

GI4E CNN Pupil center detection Iris center detection

References

  • M. Yu et al., “An easy iris center detection method for eye gaze tracking system,” J Eye Mov Res, vol. 8, no. 3, 2015, doi: 10.16910/JEMR.8.3.5.
  • K. Donuk and D. Hanbay, “Video Based Real-Time Eye Tracking,” in In: 28th IEEE Signal Processing and Communications Applications (SIU-2020), 2020, pp. 21–24.
  • Q. Zhuang, Z. Kehua, J. Wang, and Q. Chen, “Driver fatigue detection method based on eye states with pupil and iris segmentation,” IEEE Access, vol. 8, pp. 173440–173449, 2020, doi: 10.1109/ACCESS.2020.3025818.
  • H. Yan and Y. Zhang, “Detection of the pupil eigenvalues in medicine,” Proceedings - 2010 International Conference on Computational and Information Sciences, ICCIS 2010, pp. 989–992, 2010, doi: 10.1109/ICCIS.2010.244.
  • “dlib C++ Library.” http://dlib.net/ (accessed Dec. 08, 2022).
  • V. Kazemi and J. Sullivan, “One millisecond face alignment with an ensemble of regression trees,” in 2014 IEEE Conference on Computer Vision and Pattern Recognition, Jun. 2014, pp. 1867–1874. doi: 10.1109/CVPR.2014.241.
  • K. Zhang, Z. Zhang, Z. Li, and Y. Qiao, “Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks,” IEEE Signal Process Lett, vol. 23, no. 10, pp. 1499–1503, Oct. 2016, doi: 10.1109/LSP.2016.2603342.
  • “Home - OpenCV.” https://opencv.org/ (accessed Dec. 08, 2022).
  • K. Donuk and D. Hanbay, “Pupil Center Localization Based on Mini U-Net,” Computer Science, pp. 185–191, Oct. 2022, doi: 10.53070/BBD.1173482.
  • K. il Lee, J. H. Jeon, and B. C. Song, “Deep Learning-Based Pupil Center Detection for Fast and Accurate Eye Tracking System,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12364 LNCS, pp. 36–52, 2020, doi: 10.1007/978-3-030-58529-7_3/COVER.
  • A. Villanueva, V. Ponz, L. Sesma-Sanchez, M. Ariz, S. Porta, and R. Cabeza, “Hybrid method based on topography for robust detection of iris center and eye corners,” ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), vol. 9, no. 4, Aug. 2013, doi: 10.1145/2501643.2501647.
  • O. Jesorsky, K. J. Kirchberg, and R. W. Frischholz, “Robust Face Detection Using the Hausdorff Distance,” 2001, pp. 90–95. doi: 10.1007/3-540-45344-X_14.
  • A. Larumbe-Bergera, G. Garde, S. Porta, R. Cabeza, and A. Villanueva, “Accurate Pupil Center Detection in Off-the-Shelf Eye Tracking Systems Using Convolutional Neural Networks,” Sensors, vol. 21, no. 20, p. 6847, Oct. 2021, doi: 10.3390/s21206847.
There are 13 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence
Journal Section Research Articles
Authors

Kenan Donuk 0000-0002-7421-5587

Davut Hanbay 0000-0003-2271-7865

Publication Date December 28, 2022
Submission Date December 8, 2022
Published in Issue Year 2022 Volume: 3 Issue: 2

Cite

APA Donuk, K., & Hanbay, D. (2022). Real-time Iris Center Detection Based on Convolutional Neural Networks. Journal of Soft Computing and Artificial Intelligence, 3(2), 65-69. https://doi.org/10.55195/jscai.1216384
AMA Donuk K, Hanbay D. Real-time Iris Center Detection Based on Convolutional Neural Networks. JSCAI. December 2022;3(2):65-69. doi:10.55195/jscai.1216384
Chicago Donuk, Kenan, and Davut Hanbay. “Real-Time Iris Center Detection Based on Convolutional Neural Networks”. Journal of Soft Computing and Artificial Intelligence 3, no. 2 (December 2022): 65-69. https://doi.org/10.55195/jscai.1216384.
EndNote Donuk K, Hanbay D (December 1, 2022) Real-time Iris Center Detection Based on Convolutional Neural Networks. Journal of Soft Computing and Artificial Intelligence 3 2 65–69.
IEEE K. Donuk and D. Hanbay, “Real-time Iris Center Detection Based on Convolutional Neural Networks”, JSCAI, vol. 3, no. 2, pp. 65–69, 2022, doi: 10.55195/jscai.1216384.
ISNAD Donuk, Kenan - Hanbay, Davut. “Real-Time Iris Center Detection Based on Convolutional Neural Networks”. Journal of Soft Computing and Artificial Intelligence 3/2 (December 2022), 65-69. https://doi.org/10.55195/jscai.1216384.
JAMA Donuk K, Hanbay D. Real-time Iris Center Detection Based on Convolutional Neural Networks. JSCAI. 2022;3:65–69.
MLA Donuk, Kenan and Davut Hanbay. “Real-Time Iris Center Detection Based on Convolutional Neural Networks”. Journal of Soft Computing and Artificial Intelligence, vol. 3, no. 2, 2022, pp. 65-69, doi:10.55195/jscai.1216384.
Vancouver Donuk K, Hanbay D. Real-time Iris Center Detection Based on Convolutional Neural Networks. JSCAI. 2022;3(2):65-9.

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