REFERENCES
1. Casado-Aranda, L.; Sánchez-Fernández, J.; Ibáñez-Zapata, J. Evaluating communication effectiveness through eye tracking: benefits, state of the art, and unresolved questions. Int. J. Bus. Commun. 2023, 60, 24-61.
2. Liu, J.; Chi, J.; Yang, H.; Yin, X. In the eye of the beholder: a survey of gaze tracking techniques. Pattern. Recognit. 2022, 132, 108944.
3. Fan, K.; Cao, J.; Meng, Z.; et al. Predicting the reader’s English level from reading fixation patterns using the siamese convolutional neural network. IEEE. Trans. Neural. Syst. Rehabil. Eng. 2022, 30, 1071-80.
4. Wang, F. S.; Gianduzzo, C.; Meboldt, M.; Lohmeyer, Q. An algorithmic approach to determine expertise development using object-related gaze pattern sequences. Behav. Res. Methods. 2022, 54, 493-507.
5. Fan, X.; Wang, F.; Song, D.; Lu, Y.; Liu, J. GazMon: eye gazing enabled driving behavior monitoring and prediction. IEEE. Trans. Mob. Comput. 2021, 20, 1420-33.
6. Čegovnik, T.; Stojmenova, K.; Jakus, G.; Sodnik, J. An analysis of the suitability of a low-cost eye tracker for assessing the cognitive load of drivers. Appl. Ergon. 2018, 68, 1-11.
7. Li, P.; Hou, X.; Duan, X.; Yip, H.; Song, G.; Liu, Y. Appearance-based gaze estimator for natural interaction control of surgical robots. IEEE. Access. 2019, 7, 25095-110.
8. Chhimpa, G. R.; Kumar, A.; Garhwal, S.; Dhiraj. Development of a real-time eye movement-based computer interface for communication with improved accuracy for disabled people under natural head movements. J. Real. Time. Image. Proc. 2023, 20, 81.
9. Ramirez Gomez, A.; Lankes, M. Eyesthetics: making sense of the aesthetics of playing with gaze. Proc. ACM. Hum. Comput. Interact. 2021, 5, 1-24.
10. Papavlasopoulou, S.; Sharma, K.; Melhart, D.; et al. Investigating gaze interaction to support children’s gameplay. Int. J. Child. Comput. Interact. 2021, 30, 100349.
11. Deane, O.; Toth, E.; Yeo, S. H. Deep-SAGA: a deep-learning-based system for automatic gaze annotation from eye-tracking data. Behav. Res. Methods. 2023, 55, 1372-91.
12. Yuan, G.; Wang, Y.; Yan, H.; Fu, X. Self-calibrated driver gaze estimation via gaze pattern learning. Knowl. Based. Syst. 2022, 235, 107630.
13. Namnakani, O.; Abdrabou, Y.; Grizou, J.; et al. Comparing dwell time, pursuits and gaze gestures for gaze interaction on handheld mobile devices. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery; 2023. pp. 1-17.
14. Hacques, G.; Dicks, M.; Komar, J.; Seifert, L. Visual control during climbing: variability in practice fosters a proactive gaze pattern. PLoS. One. 2022, 17, e0269794.
15. Fischer, T.; Chang, H. J.; Demiris, Y. RT-GENE: real-time eye gaze estimation in natural environments. In Computer Vision - ECCV 2018. Cham: Springer International Publishing; 2018. pp. 339-57.
16. Wu, Z.; Rajendran, S.; Van As, T.; Badrinarayanan, V.; Rabinovich, A. EyeNet: a multi-task deep network for off-axis eye gaze estimation. In 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, Korea. Ocr 27-28, 2019. IEEE; 2019. pp. 3683-7.
17. Wang, Y.; Jiang, Y.; Li, J.; et al. Contrastive regression for domain adaptation on gaze estimation. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, USA. Jun 18-24, 2022. IEEE; 2022. pp. 19354-63.
18. Nonaka, S.; Nobuhara, S.; Nishino, K. Dynamic 3D gaze from afar: deep gaze estimation from temporal eye-head-body coordination. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, USA. Jun 18-24, 2022. IEEE; 2022. pp. 2182-91.
19. Fatahipour, H.; Mosavi, M. R.; Fariborz, J. Uncalibrated eye gaze estimation using SE-ResNext with unconstrained head movement and ambient light change. Research. Square. 2023.
20. Ma, C.; Baek, S.; Choi, K.; Ko, S. Improved remote gaze estimation using corneal reflection-adaptive geometric transforms. Opt. Eng. 2014, 53, 053112.
21. Shin, Y.; Choi, K.; Kim, S.; Ko, S. A novel single IR light based gaze estimation method using virtual glints. IEEE. Trans. Consum. Electron. 2015, 61, 254-60.
22. Chi, J.; Liu, J.; Wang, F.; Chi, Y.; Hou, Z. 3-D gaze-estimation method using a multi-camera-multi-light-source system. IEEE. Trans. Instrum. Meas. 2020, 69, 9695-708.
23. Liu, J.; Chi, J.; Hu, W.; Wang, Z. 3D model-based gaze tracking via iris features with a single camera and a single light source. IEEE. Trans. Human. Mach. Syst. 2021, 51, 75-86.
24. Qin, J.; Shimoyama, T.; Sugano, Y. Learning-by-novel-view-synthesis for full-face appearance-based 3D gaze estimation. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), New Orleans, USA. Jun 19-20, 2022. IEEE; 2022. pp. 4977-87.
25. Li, J.; Chen, Z.; Zhong, Y.; et al. Appearance-based gaze estimation for ASD diagnosis. IEEE. Trans. Cybern. 2022, 52, 6504-17.
26. Wang, Q.; Wang, H.; Dang, R.; et al. Style transformed synthetic images for real world gaze estimation by using residual neural network with embedded personal identities. Appl. Intell. 2023, 53, 2026-41.
27. Khanam, R.; Hussain, M. What is YOLOv5: a deep look into the internal features of the popular object detector. arXiv 2024, arXiv:2407.20892. Available online: https://doi.org/10.48550/arXiv.2407.20892. (accessed 2026-09-11).
28. Keselman, L.; Iselin Woodfill, J.; Grunnet-Jepsen, A.; Bhowmik, A. Intel(R) realSense(TM) stereoscopic depth cameras. In 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, USA. Jul 21-26, 2017. IEEE; 2017. pp. 1267-76.
29. Hu, J.; Shen, L.; Albanie, S.; Sun, G.; Wu, E. Squeeze-and-excitation networks. IEEE. Trans. Pattern. Anal. Mach. Intell. 2020, 42, 2011-23.
30. Park, S.; De Mello, S.; Molchanov, P.; Iqbal, U.; Hilliges, O.; Kautz, J. Few-shot adaptive gaze estimation. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea. Oct 27 - Nov 02, 2019. IEEE; 2019. pp. 9367-76.
31. Valliappan, N.; Dai, N.; Steinberg, E.; et al. Accelerating eye movement research via accurate and affordable smartphone eye tracking. Nat. Commun. 2020, 11, 4553.
32. Criminisi, A.; Shotton, J.; Konukoglu, E. Decision forests: a unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. Found. Trends. Comput. Graph. Vis. 2012, 7, 81-227.
33. Zhang, Y.; Cai, J.; Cui, C.; Qi, S.; Zhao, D. Predicting breast cancer response to neoadjuvant therapy by integrating radiomic and deep-learning features from early-and-peak phases of DCE-MRI. BMC. Cancer. 2025, 25, 1747.
34. Martin, D.; Fowlkes, C.; Tal, D.; Malik, J. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In Proceedings of 8th IEEE International Conference on Computer Vision. ICCV 2001, Vancouver, Canada. Jul 07-14, 2001. IEEE; 2001. pp. 416-423.
35. Doukas, M. C.; Ververas, E.; Sharmanska, V.; Zafeiriou, S. Free-HeadGAN: neural talking head synthesis with explicit gaze control. IEEE. Trans. Pattern. Anal. Mach. Intell. 2023, 45, 9743-56.
36. Arar, N. M.; Gao, H.; Thiran, J. A regression-based user calibration framework for real-time gaze estimation. IEEE. Trans. Circuits. Syst. Video. Technol. 2017, 27, 2623-38.
37. Bao, Y.; Liu, Y.; Wang, H.; Lu, F. Generalizing gaze estimation with rotation consistency. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleeans, USA. Jun 18-24, 2022. IEEE; 2022. pp. 4197-206.
38. Zhang, X.; Park, S.; Beeler, T.; Bradley, D.; Tang, S.; Hilliges, O. ETH-XGaze: a large scale dataset for gaze estimation under extreme head pose and gaze variation. In Computer Vision - ECCV 2020. Cham: Springer International Publishing; 2020. pp. 365-81.
39. Kim, J. H.; Jeong, J. W. Gaze in the dark: gaze estimation in a low-light environment with generative adversarial networks. Sensors 2020, 20, 4935.
40. Ren, Z.; Fang, F.; Hou, G.; Li, Z.; Niu, R. Appearance-based gaze estimation with feature fusion of multi-level information elements. J. Comput. Des. Eng. 2023, 10, 1080-109.
