Research on the Gaze Direction of Head-Eye Data Fusion

[1] Fuhl W, Santini T, Kasneci G. Pupilnet: Convolutional neural networks for robust pupil detection[J]. arXiv preprint arXiv:1601.04902, 2016. FuhlW SantiniT KasneciG Pupilnet: Convolutional neural networks for robust pupil detection[J] arXiv preprint arXiv:1601.04902, 2016 Search in Google Scholar

[2] Eivazi S, Santini T, Keshavarzi A. Improving real-time CNN-based pupil detection through domain-specific data augmentation[C]. Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications, 2019: 1–6. EivaziS SantiniT KeshavarziA Improving real-time CNN-based pupil detection through domain-specific data augmentation[C] Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications 2019 1 6 Search in Google Scholar

[3] Fuhl W, Geisler D, Rosenstiel W. The applicability of Cycle GANs for pupil and eyelid segmentation, data generation and image refinement[C]. Proceedings of the IEEE International Conference on Computer Vision Workshops, 2019: 0–0. FuhlW GeislerD RosenstielW The applicability of Cycle GANs for pupil and eyelid segmentation, data generation and image refinement[C] Proceedings of the IEEE International Conference on Computer Vision Workshops 2019 0 0 Search in Google Scholar

[4] Zhu J-Y, Park T, Isola P. Unpaired image-to-image translation using cycle-consistent adversarial networks[C]. Proceedings of the IEEE international conference on computer vision, 2017: 2223–2232. ZhuJ-Y ParkT IsolaP Unpaired image-to-image translation using cycle-consistent adversarial networks[C] Proceedings of the IEEE international conference on computer vision 2017 2223 2232 Search in Google Scholar

[5] Tsironi E, Barros P, Weber C. An analysis of convolutional long short-term memory recurrent neural networks for gesture recognition[J]. Neurocomputing, 2017, 268: 76–86. TsironiE BarrosP WeberC An analysis of convolutional long short-term memory recurrent neural networks for gesture recognition[J] Neurocomputing 2017 268 76 86 Search in Google Scholar

[6] Liu Z, Li J, Shen Z. Learning efficient convolutional networks through network slimming[C]. Proceedings of the IEEE International Conference on Computer Vision, 2017: 2736–2744. LiuZ LiJ ShenZ Learning efficient convolutional networks through network slimming[C] Proceedings of the IEEE International Conference on Computer Vision 2017 2736 2744 Search in Google Scholar

[7] Bao J, Ye M. Head pose estimation based on robust convolutional neural network[J]. Cybernetics and Information Technologies, 2016, 16(6): 133–145. BaoJ YeM Head pose estimation based on robust convolutional neural network[J] Cybernetics and Information Technologies 2016 16 6 133 145 Search in Google Scholar

[8] Patacchiola M, Cangelosi A. Head pose estimation in the wild using convolutional neural networks and adaptive gradient methods[J]. Pattern Recognition, 2017, 71: 132–143. PatacchiolaM CangelosiA Head pose estimation in the wild using convolutional neural networks and adaptive gradient methods[J] Pattern Recognition 2017 71 132 143 Search in Google Scholar

[9] Santini T, Niehorster D C, Kasneci E. Get a grip: slippage-robust and glint-free gaze estimation for real-time pervasive head-mounted eye tracking[C]. Proceedings of the 11th ACM symposium on eye tracking research & applications, 2019: 1–10. SantiniT NiehorsterD C KasneciE Get a grip: slippage-robust and glint-free gaze estimation for real-time pervasive head-mounted eye tracking[C] Proceedings of the 11th ACM symposium on eye tracking research & applications 2019 1 10 Search in Google Scholar

[10] Su Haiming, Hou Zhenjie, Liang Jiuzhen. A gaze tracking method using geometric features of human eyes [J]. Journal of Image and Graphics, 2019(201906): 914–923. SuHaiming HouZhenjie LiangJiuzhen A gaze tracking method using geometric features of human eyes [J] Journal of Image and Graphics 2019 201906 914 923 Search in Google Scholar

[11] Park S, Spurr A, Hilliges O. Deep pictorial gaze estimation[C]. Proceedings of the European Conference on Computer Vision (ECCV), 2018: 721–738. ParkS SpurrA HilligesO Deep pictorial gaze estimation[C] Proceedings of the European Conference on Computer Vision (ECCV) 2018 721 738 Search in Google Scholar

[12] Zhang X, Sugano Y, Fritz M. Mpiigaze: Real-world dataset and deep appearance-based gaze estimation[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 41(1): 162–175. ZhangX SuganoY FritzM Mpiigaze: Real-world dataset and deep appearance-based gaze estimation[J] IEEE transactions on pattern analysis and machine intelligence 2017 41 1 162 175 Search in Google Scholar

[13] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition [J]. arXiv preprint arXiv:1409.1556, 2014. SimonyanK ZissermanA Very deep convolutional networks for large-scale image recognition [J] arXiv preprint arXiv:1409.1556, 2014 Search in Google Scholar

[14] Mase K, Watanabe Y, Suenaga Y. Real-time head motion detection system. Sensing and Reconstruction of Three-Dimensional Objects and Scenes: International Society for Optics and Photonics; 1990. p. 262–8. MaseK WatanabeY SuenagaY Real-time head motion detection system Sensing and Reconstruction of Three-Dimensional Objects and Scenes: International Society for Optics and Photonics 1990 262 8 Search in Google Scholar

[15] Rolland JP, Davis LD, Baillot Y. A survey of tracking technologies for virtual environments. Fundamentals of wearable computers and augmented reality: CRC Press; 2001. p. 83–128. RollandJP DavisLD BaillotY A survey of tracking technologies for virtual environments Fundamentals of wearable computers and augmented reality: CRC Press 2001 83 128 Search in Google Scholar

[16] Zhou H, Hu HJBsp, control. Human motion tracking for rehabilitation—A survey. 2008;3:1–18. ZhouH Hu HJBsp, control Human motion tracking for rehabilitation—A survey 2008 3 1 18 Search in Google Scholar

[17] Al-Rahayfeh A, Faezipour MJIjoteih, medicine. Eye tracking and head movement detection: A state-of-art survey. 2013; 1:2100212. Al-RahayfehA FaezipourM JIjoteih, Eye tracking and head movement detection: A state-of-art survey 2013 1 2100212 Search in Google Scholar

[18] Von Lüdinghausen MJCATOJotAAoCA, Anatomists tBAoC. Bilateral supernumerary rectus muscles of the orbit. 1998; 11:271–7. Von Lüdinghausen MJCATOJotAAoCA, Anatomists tBAoC Bilateral supernumerary rectus muscles of the orbit 1998 11 271 7 Search in Google Scholar

[19] Raudonis V, Simutis R, Narvydas G. Discrete eye tracking for medical applications. 2009 2nd International Symposium on Applied Sciences in Biomedical and Communication Technologies: IEEE; 2009. p. 1–6. RaudonisV SimutisR NarvydasG Discrete eye tracking for medical applications 2009 2nd International Symposium on Applied Sciences in Biomedical and Communication Technologies: IEEE 2009 1 6 Search in Google Scholar

[20] Botha CP, de Graaf T, Schutte S, Root R, Wielopolski P, van der Helm FC, et al. MRI-based visualisation of orbital fat deformation during eye motion. Visualization in medicine and life sciences: Springer; 2008. p. 221–33. BothaCP de GraafT SchutteS RootR WielopolskiP van der HelmFC MRI-based visualisation of orbital fat deformation during eye motion Visualization in medicine and life sciences Springer 2008 221 33 Search in Google Scholar

[21] Glarin RK, Nguyen BN, Cleary JO, Kolbe SC, Ordidge RJ, Bui BV, et al. Mr-eye: high-resolution mri of the human eye and orbit at ultrahigh field (7t). 2021; 29:103–16. GlarinRK NguyenBN ClearyJO KolbeSC OrdidgeRJ BuiBV Mr-eye: high-resolution mri of the human eye and orbit at ultrahigh field (7t) 2021 29 103 16 Search in Google Scholar

[22] Uhl A, Wild P. Multi-stage visible wavelength and near infrared iris segmentation framework. International Conference Image Analysis and Recognition: Springer; 2012. p. 1–10. UhlA WildP Multi-stage visible wavelength and near infrared iris segmentation framework International Conference Image Analysis and Recognition Springer 2012 1 10 Search in Google Scholar

[23] Loskutova E, Butler JS, Hernandez Martinez G, Flitcroft I, Loughman JJCER. Macular Pigment Optical Density Fluctuation as a Function of Pupillary Mydriasis: Methodological Considerations for Dual-Wavelength Autofluorescence. 2021; 46:532–8. LoskutovaE ButlerJS Hernandez MartinezG FlitcroftI LoughmanJJCER Macular Pigment Optical Density Fluctuation as a Function of Pupillary Mydriasis: Methodological Considerations for Dual-Wavelength Autofluorescence 2021 46 532 8 Search in Google Scholar

[24] Jan F, Usman IJO. Iris segmentation for visible wavelength and near infrared eye images. 2014; 125:4274–82. JanF UsmanIJO Iris segmentation for visible wavelength and near infrared eye images 2014 125 4274 82 Search in Google Scholar

[25] Chen Y, Davoine F. Simultaneous Tracking of Rigid Head Motion and Non-rigid Facial Animation by Analyzing Local Features Statistically. BMVC2006. ChenY DavoineF Simultaneous Tracking of Rigid Head Motion and Non-rigid Facial Animation by Analyzing Local Features Statistically BMVC2006. Search in Google Scholar