20.500.12556/RUL-182358 Exploring transfer learning for gaze estimation a study on model adaptability This study explores the use of transfer learning in gaze estimation, focusing on the development of personalized models tailored to individual users. Our approach involves collecting gaze data using standard laptop webcams, designed to operate effectively within resource-limited settings, thereby enhancing accessibility and affordability. We conducted a comparative analysis of models using transfer learning against models trained without pre-trained weights, examining their convergence behavior and sensitivity to different dataset sizes. The analysis includes both eye and face images, providing a comprehensive view of model adaptability. Our findings show that while both methods produce comparable results overall, transfer learning offers notable advantages—particularly faster convergence, reduced computational cost, and enhanced stability when data are limited. However, the results also reveal that transfer learning is not universally superior; for face images, models trained from scratch achieved lower mean errors but exhibited higher variability, whereas transfer learning ensured more consistent performance. These insights highlight that the benefits of transfer learning depend on the data characteristics and task complexity. In the most data-constrained setting (100 images), transfer learning reduced the mean error by 20.99 px for the Left Eye model and 35.56 px for the Right Eye model, whereas for face images the models trained from scratch consistently achieved lower mean error across all evaluated dataset sizes. Overall, this study underscores the potential of transfer learning for efficient and scalable gaze estimation, particularly in small-data environments, while providing practical guidance on when and how transfer learning yields the greatest benefit for real-time applications such as human–computer interaction (HCI), assistive technologies, and personalized user experiences. gaze estimation transfer learning human-computer interaction ocena pogleda učenje s prenosom interakcija človek-računalnik true false true Angleški jezik Slovenski jezik Članek v reviji 2026-05-08 08:41:23 2026-05-08 08:41:24 2026-05-09 04:44:15 0000-00-00 00:00:00 2026 0 0 31 str. iss. 9, art. 4540 Vol. 16 May 2026 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.93:004.8:004.5 2076-3417 10.3390/app16094540 277444867 RAZ_Faizan_Ansari_Mohd_2026.pdf RAZ_Faizan_Ansari_Mohd_2026.pdf 1 4C1D6B3AE9E5824D9D16C8EB01B60A2B 165d50a55615d3ac275b3504ea766ee6201c3a4897c6acd9581d8ff4cbfc4671 f1ca6558-4aac-11f1-b0ab-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=233527 https://www.mdpi.com/2076-3417/16/9/4540 1 a3c6fdd6-4aa8-11f1-b0ab-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=233524 Fakulteta za računalništvo in informatiko 0 0 0