{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T06:46:04Z","timestamp":1774593964872,"version":"3.50.1"},"reference-count":172,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T00:00:00Z","timestamp":1774396800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Federal Ministry of Research, Technology, and Space of Germany","award":["13N17576"],"award-info":[{"award-number":["13N17576"]}]},{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"crossref","award":["ZS\/2023\/12\/182056"],"award-info":[{"award-number":["ZS\/2023\/12\/182056"]}],"id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"crossref","award":["ZS\/2023\/12\/182322"],"award-info":[{"award-number":["ZS\/2023\/12\/182322"]}],"id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"crossref"}]},{"name":"European Union and the state of Saxony-Anhalt"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotics"],"abstract":"<jats:p>Gaze estimation, a critical facet of understanding user intent and enhancing human\u2013computer interaction, has seen substantial advancements with the integration of deep learning technologies. Despite the progress, the application of deep learning in gaze estimation presents unique challenges, notably in the adaptation and optimization of these models for precise gaze tracking. This paper conducts a thorough review of recent developments in deep learning-based gaze estimation, with a particular focus on the evolution from traditional methods to sophisticated appearance-based techniques. We examine the key components of successful gaze estimation systems, including input feature processing, neural network architectures, and the importance of data preprocessing in achieving high accuracy. Our analysis extends to a comprehensive comparison of existing methods, shedding light on their effectiveness and limitations within various implementation contexts. Through this systematic review, we aim to consolidate existing knowledge in the field, identify gaps in current research, and suggest directions for future investigation. By providing a clear overview of the state-of-the-art in gaze estimation and discussing ongoing challenges and potential solutions, our work seeks to inspire further innovation and progress in developing more accurate and efficient gaze estimation systems.<\/jats:p>","DOI":"10.3390\/robotics15040069","type":"journal-article","created":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T15:10:24Z","timestamp":1774537824000},"page":"69","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning-Based Gaze Estimation: A Review"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1575-0942","authenticated-orcid":false,"given":"Ahmed A.","family":"Abdelrahman","sequence":"first","affiliation":[{"name":"Neuro-Information Technology, Otto-von-Guericke-University Magdeburg, 39106 Magdeburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5716-7587","authenticated-orcid":false,"given":"Basheer","family":"Al-Tawil","sequence":"additional","affiliation":[{"name":"Neuro-Information Technology, Otto-von-Guericke-University Magdeburg, 39106 Magdeburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3632-2402","authenticated-orcid":false,"given":"Ayoub","family":"Al-Hamadi","sequence":"additional","affiliation":[{"name":"Neuro-Information Technology, Otto-von-Guericke-University Magdeburg, 39106 Magdeburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.dcn.2016.11.001","article-title":"Beyond eye gaze: What else can eyetracking reveal about cognition and cognitive development?","volume":"25","author":"Eckstein","year":"2017","journal-title":"Dev. Cogn. Neurosci."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Raptis, G.E., Katsini, C., Belk, M., Fidas, C., Samaras, G., and Avouris, N. (2017). Using eye gaze data and visual activities to infer human cognitive styles: Method and feasibility studies. UMAP\u201917 Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, ACM.","DOI":"10.1145\/3079628.3079690"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1177\/1094428117744882","article-title":"The promise of eye-tracking methodology in organizational research: A taxonomy, review, and future avenues","volume":"22","author":"Oll","year":"2019","journal-title":"Organ. Res. Methods"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1002\/eat.22998","article-title":"Eye-tracking research in eating disorders: A systematic review","volume":"52","author":"Harrison","year":"2019","journal-title":"Int. J. Eat. Disord."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Hempel, T., and Al-Hamadi, A. (2020). Slam-based multistate tracking system for mobile human-robot interaction. International Conference on Image Analysis and Recognition, Springer.","DOI":"10.1007\/978-3-030-50347-5_32"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Strazdas, D., Hintz, J., Khalifa, A., Abdelrahman, A.A., Hempel, T., and Al-Hamadi, A. (2022). Robot System Assistant (RoSA): Towards Intuitive Multi-Modal and Multi-Device Human-Robot Interaction. Sensors, 22.","DOI":"10.3390\/s22030923"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"61980","DOI":"10.1109\/ACCESS.2022.3182469","article-title":"Multi-Modal Engagement Prediction in Multi-Person Human-Robot Interaction","volume":"10","author":"Abdelrahman","year":"2022","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Khalifa, A., Abdelrahman, A.A., Strazdas, D., Hintz, J., Hempel, T., and Al-Hamadi, A. (2022). Face recognition and tracking framework for human\u2013robot interaction. Appl. Sci., 12.","DOI":"10.3390\/app12115568"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1800","DOI":"10.1109\/TIE.2021.3057033","article-title":"Data-driven estimation of driver attention using calibration-free eye gaze and scene features","volume":"69","author":"Hu","year":"2021","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1109\/TIV.2018.2843120","article-title":"Driver gaze zone estimation using convolutional neural networks: A general framework and ablative analysis","volume":"3","author":"Vora","year":"2018","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ghosh, S., Dhall, A., Sharma, G., Gupta, S., and Sebe, N. (2021). Speak2label: Using domain knowledge for creating a large scale driver gaze zone estimation dataset. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCVW54120.2021.00324"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1720","DOI":"10.1109\/TPAMI.2018.2845370","article-title":"Predicting the Driver\u2019s Focus of Attention: The DR (eye) VE Project","volume":"41","author":"Palazzi","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"8715","DOI":"10.1109\/TITS.2021.3085492","article-title":"An analysis of driver gaze behaviour at roundabouts","volume":"23","author":"Abbasi","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Hempel, T., Abdelrahman, A.A., and Al-Hamadi, A. (2022). 6d rotation representation for unconstrained head pose estimation. Proceedings of the 2022 IEEE International Conference on Image Processing (ICIP), IEEE.","DOI":"10.1109\/ICIP46576.2022.9897219"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2980179.2980246","article-title":"Towards foveated rendering for gaze-tracked virtual reality","volume":"35","author":"Patney","year":"2016","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3","DOI":"10.16910\/jemr.12.1.3","article-title":"Eye tracking in virtual reality","volume":"12","author":"Clay","year":"2019","journal-title":"J. Eye Mov. Res."},{"key":"ref_17","first-page":"242","article-title":"Essai sur la physiologie de la lecture","volume":"82","author":"Javal","year":"1879","journal-title":"Ann. D\u2019Ocul."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1109\/ICPR.2000.902895","article-title":"A calibration-free gaze tracking technique","volume":"Volume 4","author":"Shih","year":"2000","journal-title":"Proceedings of the 15th International Conference on Pattern Recognition. ICPR-2000"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, X., Sugano, Y., Fritz, M., and Bulling, A. (2015). Appearance-based gaze estimation in the wild. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2015.7299081"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"575","DOI":"10.2307\/1412192","article-title":"Preliminary experiments in the physiology and psychology of reading","volume":"9","author":"Huey","year":"1898","journal-title":"Am. J. Psychol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"283","DOI":"10.2307\/1412745","article-title":"On the psychology and physiology of reading","volume":"11","author":"Huey","year":"1900","journal-title":"I. Am. J. Psychol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"572","DOI":"10.2307\/1412191","article-title":"A method of recording eye-movements","volume":"9","author":"Delabarre","year":"1898","journal-title":"Am. J. Psychol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1037\/h0076100","article-title":"The angle velocity of eye movements","volume":"8","author":"Dodge","year":"1901","journal-title":"Psychol. Rev."},{"key":"ref_24","unstructured":"Buswell, G.T. (1935). How People Look at Pictures: A Study of the Psychology and Perception in Art, American Psychological Association."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yarbus, A.L., and Yarbus, A.L. (1967). Eye movements during perception of complex objects. Eye Movements and Vision, Springer.","DOI":"10.1007\/978-1-4899-5379-7"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1136\/bjo.32.9.581","article-title":"Methods of investigating eye movements","volume":"32","author":"Hartridge","year":"1948","journal-title":"Br. J. Ophthalmol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1109\/TBME.1974.324318","article-title":"Remote measurement of eye direction allowing subject motion over one cubic foot of space","volume":"4","author":"Merchant","year":"1974","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_28","unstructured":"Levine, J.L. (1981). An Eye-Controlled Computer, IBM Research Division, TJ Watson Research Center."},{"key":"ref_29","unstructured":"Ellis, S., Candrea, R., Misner, J., Craig, C.S., Lankford, C.P., and Hutchinson, T.E. (1998). Windows to the soul? What eye movements tell us about software usability. Proceedings of the Usability Professionals\u2019 Association Conference, UPA Press."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yamazoe, H., Utsumi, A., Yonezawa, T., and Abe, S. (2008). Remote gaze estimation with a single camera based on facial-feature tracking without special calibration actions. Proceedings of the 2008 Symposium on Eye Tracking Research & Applications, ACM.","DOI":"10.1145\/1344471.1344527"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1109\/TPAMI.2009.30","article-title":"In the eye of the beholder: A survey of models for eyes and gaze","volume":"32","author":"Hansen","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1647","DOI":"10.1109\/CISP.2010.5647733","article-title":"Gazing estimation and correction from elliptical features of one iris","volume":"Volume 4","author":"Zhang","year":"2010","journal-title":"Proceedings of the 2010 3rd International Congress on Image and Signal Processing"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Tsukada, A., Shino, M., Devyver, M., and Kanade, T. (2011). Illumination-free gaze estimation method for first-person vision wearable device. Proceedings of the 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), IEEE.","DOI":"10.1109\/ICCVW.2011.6130505"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wood, E., and Bulling, A. (2014). Eyetab: Model-based gaze estimation on unmodified tablet computers. ETRA\u201914 Proceedings of the Symposium on Eye Tracking Research and Applications, ACM.","DOI":"10.1145\/2578153.2578185"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2033","DOI":"10.1109\/TPAMI.2014.2313123","article-title":"Adaptive linear regression for appearance-based gaze estimation","volume":"36","author":"Lu","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_36","unstructured":"Venkateswarlu, R. (2003). Eye gaze estimation from a single image of one eye. Proceedings of the Proceedings Ninth IEEE International Conference on Computer Vision, IEEE."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1109\/TPAMI.2017.2778103","article-title":"Mpiigaze: Real-world dataset and deep appearance-based gaze estimation","volume":"41","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhang, X., Sugano, Y., Fritz, M., and Bulling, A. (2017). It\u2019s written all over your face: Full-face appearance-based gaze estimation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, IEEE.","DOI":"10.1109\/CVPRW.2017.284"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Fischer, T., Chang, H.J., and Demiris, Y. (2018). Rt-gene: Real-time eye gaze estimation in natural environments. Proceedings of the European Conference on Computer Vision (ECCV), Springer.","DOI":"10.1007\/978-3-030-01249-6_21"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1609\/aaai.v36i1.19921","article-title":"Puregaze: Purifying gaze feature for generalizable gaze estimation","volume":"Volume 36","author":"Cheng","year":"2022","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"O Oh, J., Chang, H.J., and Choi, S.I. (2022). Self-Attention with Convolution and Deconvolution for Efficient Eye Gaze Estimation From a Full Face Image. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPRW56347.2022.00547"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Bao, Y., Liu, Y., Wang, H., and Lu, F. (2022). Generalizing Gaze Estimation With Rotation Consistency. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR52688.2022.00417"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Liu, Y., Liu, R., Wang, H., and Lu, F. (2021). Generalizing gaze estimation with outlier-guided collaborative adaptation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, IEEE.","DOI":"10.1109\/ICCV48922.2021.00381"},{"key":"ref_44","unstructured":"Garbin, S.J., Shen, Y., Schuetz, I., Cavin, R., Hughes, G., and Talathi, S.S. (2019). Openeds: Open eye dataset. arXiv."},{"key":"ref_45","unstructured":"Palmero, C., Sharma, A., Behrendt, K., Krishnakumar, K., Komogortsev, O.V., and Talathi, S.S. (2020). OpenEDS2020: Open eyes dataset. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Kim, J., Stengel, M., Majercik, A., De Mello, S., Dunn, D., Laine, S., McGuire, M., and Luebke, D. (2019). Nvgaze: An anatomically-informed dataset for low-latency, near-eye gaze estimation. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, ACM.","DOI":"10.1145\/3290605.3300780"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3130971","article-title":"Invisibleeye: Mobile eye tracking using multiple low-resolution cameras and learning-based gaze estimation","volume":"Volume 1","author":"Tonsen","year":"2017","journal-title":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Wu, Z., Rajendran, S., Van As, T., Badrinarayanan, V., and Rabinovich, A. (2019). Eyenet: A multi-task deep network for off-axis eye gaze estimation. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW), IEEE.","DOI":"10.1109\/ICCVW.2019.00455"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Park, S., Spurr, A., and Hilliges, O. (2018). Deep pictorial gaze estimation. Proceedings of the European Conference on Computer Vision (ECCV), Springer.","DOI":"10.1007\/978-3-030-01261-8_44"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Zhang, X., Sugano, Y., and Bulling, A. (2019). Evaluation of appearance-based methods and implications for gaze-based applications. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, ACM.","DOI":"10.1145\/3290605.3300646"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Dubey, N., Ghosh, S., and Dhall, A. (2019). Unsupervised learning of eye gaze representation from the web. Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), IEEE.","DOI":"10.1109\/IJCNN.2019.8851961"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Park, S., Mello, S.D., Molchanov, P., Iqbal, U., Hilliges, O., and Kautz, J. (2019). Few-shot adaptive gaze estimation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, IEEE.","DOI":"10.1109\/ICCV.2019.00946"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Yu, Y., Liu, G., and Odobez, J.M. (2018). Deep multitask gaze estimation with a constrained landmark-gaze model. Proceedings of the European Conference on Computer Vision (ECCV) Workshops, Springer.","DOI":"10.1007\/978-3-030-11012-3_35"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Chong, E., Ruiz, N., Wang, Y., Zhang, Y., Rozga, A., and Rehg, J.M. (2018). Connecting gaze, scene, and attention: Generalized attention estimation via joint modeling of gaze and scene saliency. Proceedings of the European Conference on Computer Vision (ECCV), Springer.","DOI":"10.1007\/978-3-030-01228-1_24"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Zhang, X., Huang, M.X., Sugano, Y., and Bulling, A. (2018). Training person-specific gaze estimators from user interactions with multiple devices. CHI\u201918 Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, ACM.","DOI":"10.1145\/3173574.3174198"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Chen, Z., and Shi, B.E. (2018). Appearance-based gaze estimation using dilated-convolutions. Proceedings of the Asian Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-030-20876-9_20"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Zhou, X., Lin, J., Jiang, J., and Chen, S. (2019). Learning a 3D gaze estimator with improved Itracker combined with bidirectional LSTM. Proceedings of the 2019 IEEE International Conference on Multimedia and Expo (ICME), IEEE.","DOI":"10.1109\/ICME.2019.00151"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Wang, K., Su, H., and Ji, Q. (2019). Neuro-inspired eye tracking with eye movement dynamics. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2019.01006"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Zhang, X., Sugano, Y., Bulling, A., and Hilliges, O. (2020). Learning-based region selection for end-to-end gaze estimation. Proceedings of the 31st British Machine Vision Conference (BMVC 2020), British Machine Vision Association.","DOI":"10.5244\/C.34.22"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2488","DOI":"10.1609\/aaai.v33i01.33012488","article-title":"RGBD based gaze estimation via multi-task CNN","volume":"Volume 33","author":"Lian","year":"2019","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Tawari, A., Chen, K.H., and Trivedi, M.M. (2014). Where is the driver looking: Analysis of head, eye and iris for robust gaze zone estimation. Proceedings of the 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), IEEE.","DOI":"10.1109\/ITSC.2014.6957817"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Santini, T., Fuhl, W., and Kasneci, E. (2017). Calibme: Fast and unsupervised eye tracker calibration for gaze-based pervasive human-computer interaction. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, ACM.","DOI":"10.1145\/3025453.3025950"},{"key":"ref_63","unstructured":"Cheng, Y., Wang, H., Bao, Y., and Lu, F. (2021). Appearance-based gaze estimation with deep learning: A review and benchmark. arXiv."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"142581","DOI":"10.1109\/ACCESS.2020.3013540","article-title":"Convolutional neural network-based methods for eye gaze estimation: A survey","volume":"8","author":"Akinyelu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"114037","DOI":"10.1016\/j.eswa.2020.114037","article-title":"Eye tracking algorithms, techniques, tools, and applications with an emphasis on machine learning and Internet of Things technologies","volume":"166","author":"Klaib","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"116894","DOI":"10.1016\/j.eswa.2022.116894","article-title":"Eye gaze estimation: A survey on deep learning-based approaches","volume":"199","author":"Pathirana","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/TPAMI.2023.3321337","article-title":"Automatic gaze analysis: A survey of deep learning based approaches","volume":"46","author":"Ghosh","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"16495","DOI":"10.1109\/ACCESS.2017.2735633","article-title":"A review and analysis of eye-gaze estimation systems, algorithms and performance evaluation methods in consumer platforms","volume":"5","author":"Kar","year":"2017","journal-title":"IEEE Access"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Cazzato, D., Leo, M., Distante, C., and Voos, H. (2020). When i look into your eyes: A survey on computer vision contributions for human gaze estimation and tracking. Sensors, 20.","DOI":"10.3390\/s20133739"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","article-title":"A computational approach to edge detection","volume":"PAMI-8","author":"Canny","year":"1986","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.cviu.2004.07.013","article-title":"Eye tracking in the wild","volume":"98","author":"Hansen","year":"2005","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Valenti, R., and Gevers, T. (2008). Accurate eye center location and tracking using isophote curvature. Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2008.4587529"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Xiong, X., Liu, Z., Cai, Q., and Zhang, Z. (2014). Eye gaze tracking using an RGBD camera: A comparison with a RGB solution. Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, ACM.","DOI":"10.1145\/2638728.2641694"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Alberto Funes Mora, K., and Odobez, J.M. (2014). Geometric generative gaze estimation (g3e) for remote rgb-d cameras. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2014.229"},{"key":"ref_75","unstructured":"Ishikawa, T. (2004). Passive Driver Gaze Tracking with Active Appearance Models. [Master\u2019s Thesis, Link\u00f6ping University]."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Baltrusaitis, T., Robinson, P., and Morency, L.P. (2013). Constrained local neural fields for robust facial landmark detection in the wild. Proceedings of the IEEE International Conference on Computer Vision Workshops, IEEE.","DOI":"10.1109\/ICCVW.2013.54"},{"key":"ref_77","unstructured":"Wu, H., Kitagawa, Y., Wada, T., Kato, T., and Chen, Q. (2007). Tracking iris contour with a 3D eye-model for gaze estimation. Proceedings of the Computer Vision\u2013ACCV 2007: 8th Asian Conference on Computer Vision, Tokyo, Japan, 18\u201322 November 2007, Proceedings, Part I 8, Springer."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Wang, K., and Ji, Q. (2017). Real time eye gaze tracking with 3d deformable eye-face model. Proceedings of the IEEE International Conference on Computer Vision, IEEE.","DOI":"10.1109\/ICCV.2017.114"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Wood, E., Baltru\u0161aitis, T., Morency, L.P., Robinson, P., and Bulling, A. (2016). A 3d morphable eye region model for gaze estimation. Proceedings of the Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, 11\u201314 October 2016, Proceedings, Part I 14, Springer.","DOI":"10.1007\/978-3-319-46448-0_18"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1111\/cgf.13355","article-title":"Gazedirector: Fully articulated eye gaze redirection in video","volume":"37","author":"Wood","year":"2018","journal-title":"Comput. Graph. Forum"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1124","DOI":"10.1109\/TBME.2005.863952","article-title":"General theory of remote gaze estimation using the pupil center and corneal reflections","volume":"53","author":"Guestrin","year":"2006","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.cviu.2004.07.011","article-title":"A novel non-intrusive eye gaze estimation using cross-ratio under large head motion","volume":"98","author":"Yoo","year":"2005","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Cerrolaza, J.J., Villanueva, A., and Cabeza, R. (2008). Taxonomic study of polynomial regressions applied to the calibration of video-oculographic systems. Proceedings of the 2008 Symposium on Eye Tracking Research & Applications, ACM.","DOI":"10.1145\/1344471.1344530"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Sesma, L., Villanueva, A., and Cabeza, R. (2012). Evaluation of pupil center-eye corner vector for gaze estimation using a web cam. Proceedings of the Symposium on Eye Tracking Research and Applications, ACM.","DOI":"10.1145\/2168556.2168598"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Huang, M.X., Kwok, T.C., Ngai, G., Chan, S.C., and Leong, H.V. (2016). Building a personalized, auto-calibrating eye tracker from user interactions. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, ACM.","DOI":"10.1145\/2858036.2858404"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Bulling, A., and Gellersen, H. (2013). Sideways: A gaze interface for spontaneous interaction with situated displays. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM.","DOI":"10.1145\/2470654.2470775"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Zhang, Y., M\u00fcller, J., Chong, M.K., Bulling, A., and Gellersen, H. (2014). Gazehorizon: Enabling passers-by to interact with public displays by gaze. Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, ACM.","DOI":"10.1145\/2632048.2636071"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Hansen, D.W., Hansen, J.P., Nielsen, M., Johansen, A.S., and Stegmann, M.B. (2002). Eye typing using Markov and active appearance models. Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision, 2002. (WACV 2002). Proceedings, IEEE.","DOI":"10.1109\/ACV.2002.1182170"},{"key":"ref_89","unstructured":"Valenti, R., Staiano, J., Sebe, N., and Gevers, T. (2009). Webcam-based visual gaze estimation. Proceedings of the Image Analysis and Processing\u2013ICIAP 2009: 15th International Conference Vietri sul Mare, Italy, 8\u201311 September 2009 Proceedings 15, Springer."},{"key":"ref_90","unstructured":"B\u00e4ck, D. (2026, March 01). Neural Network Gaze Tracking Using Web Camera. Available online: https:\/\/www.diva-portal.org\/smash\/get\/diva2:21388\/FULLTEXT01.pdf."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.image.2015.05.007","article-title":"On visual gaze tracking based on a single low cost camera","volume":"36","author":"Skodras","year":"2015","journal-title":"Signal Process. Image Commun."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Huang, M.X., Kwok, T.C., Ngai, G., Leong, H.V., and Chan, S.C. (2014). Building a self-learning eye gaze model from user interaction data. Proceedings of the 22nd ACM International Conference on Multimedia, ACM.","DOI":"10.1145\/2647868.2655031"},{"key":"ref_93","unstructured":"Baluja, S., and Pomerleau, D. (1993). Non-intrusive gaze tracking using artificial neural networks. Advances in Neural Information Processing Systems 6, Morgan Kaufmann."},{"key":"ref_94","unstructured":"Pomerleau, D., and Baluja, S. (1993). Non-intrusive gaze tracking using artificial neural networks. Proceedings of the AAAI Fall Symposium on Machine Learning in Computer Vision, Raleigh, NC, AAAI."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Tan, K.H., Kriegman, D.J., and Ahuja, N. (2002). Appearance-based eye gaze estimation. Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision, 2002. (WACV 2002). Proceedings, IEEE.","DOI":"10.1109\/ACV.2002.1182180"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1109\/CVPR.2006.285","article-title":"Sparse and Semi-supervised Visual Mapping with the S^ 3GP","volume":"Volume 1","author":"Williams","year":"2006","journal-title":"Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201906)"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1109\/TPAMI.2012.101","article-title":"Appearance-based gaze estimation using visual saliency","volume":"35","author":"Sugano","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Lu, F., Sugano, Y., Okabe, T., and Sato, Y. (2011). Inferring human gaze from appearance via adaptive linear regression. Proceedings of the 2011 International Conference on Computer Vision, IEEE.","DOI":"10.1109\/ICCV.2011.6126237"},{"key":"ref_99","unstructured":"Mora, K.A.F., and Odobez, J.M. (2013). Person independent 3d gaze estimation from remote rgb-d cameras. Proceedings of the 2013 IEEE International Conference on Image Processing, IEEE."},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Sugano, Y., Matsushita, Y., and Sato, Y. (2014). Learning-by-synthesis for appearance-based 3d gaze estimation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2014.235"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.neucom.2015.07.125","article-title":"Person-independent eye gaze prediction from eye images using patch-based features","volume":"182","author":"Lu","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_102","unstructured":"Sugano, Y., Matsushita, Y., Sato, Y., and Koike, H. (2008). An incremental learning method for unconstrained gaze estimation. Proceedings of the Computer Vision\u2013ECCV 2008: 10th European Conference on Computer Vision, Marseille, France, 12\u201318 October 2008, Proceedings, Part III 10, Springer."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.imavis.2014.01.005","article-title":"Learning gaze biases with head motion for head pose-free gaze estimation","volume":"32","author":"Lu","year":"2014","journal-title":"Image Vis. Comput."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"3680","DOI":"10.1109\/TIP.2015.2445295","article-title":"Gaze estimation from eye appearance: A head pose-free method via eye image synthesis","volume":"24","author":"Lu","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Lu, F., Okabe, T., Sugano, Y., and Sato, Y. (2011). A head pose-free approach for appearance-based gaze estimation. Proceedings of the British Machine Vision Conference (BMVC), BMVA Press.","DOI":"10.5244\/C.25.126"},{"key":"ref_106","unstructured":"Huang, Q. (2015). TabletGaze: Dataset and Algorithm for Unconstrained Appearance-Based Gaze Estimation in Mobile Tablets. [Ph.D. Thesis, Rice University]."},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"10623","DOI":"10.1609\/aaai.v34i07.6636","article-title":"A coarse-to-fine adaptive network for appearance-based gaze estimation","volume":"Volume 34","author":"Cheng","year":"2020","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"ref_109","unstructured":"Murthy, L., and Biswas, P. (2021). Appearance-based gaze estimation using attention and difference mechanism. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"5510","DOI":"10.1109\/TCSVT.2022.3152800","article-title":"Gaze estimation via modulation-based adaptive network with auxiliary self-learning","volume":"32","author":"Wu","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Wang, H., Oh, J.O., Chang, H.J., Na, J.H., Tae, M., Zhang, Z., and Choi, S.I. (2023). GazeCaps: Gaze Estimation With Self-Attention-Routed Capsules. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPRW59228.2023.00267"},{"key":"ref_112","unstructured":"Machin, D. (1998). A novel approach to real-time non-intrusive gaze finding. Proceedings of the Ninth British Machine Vision Conference, University of Southampton Institutional Repository."},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Cheng, Y., Lu, F., and Zhang, X. (2018). Appearance-based gaze estimation via evaluation-guided asymmetric regression. Proceedings of the European Conference on Computer Vision (ECCV), Springer.","DOI":"10.1007\/978-3-030-01264-9_7"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1007\/s00138-017-0852-4","article-title":"Tabletgaze: Dataset and analysis for unconstrained appearance-based gaze estimation in mobile tablets","volume":"28","author":"Huang","year":"2017","journal-title":"Mach. Vis. Appl."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.patcog.2017.06.009","article-title":"Head pose estimation in the wild using convolutional neural networks and adaptive gradient methods","volume":"71","author":"Patacchiola","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Kim, J.H., and Jeong, J.W. (2020). Gaze estimation in the dark with generative adversarial networks. ETRA\u201920 Adjunct: Proceedings of the ACM Symposium on Eye Tracking Research and Applications, ACM.","DOI":"10.1145\/3379157.3391654"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"2340","DOI":"10.1109\/TIP.2021.3051462","article-title":"Enlightengan: Deep light enhancement without paired supervision","volume":"30","author":"Jiang","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Rangesh, A., Zhang, B., and Trivedi, M.M. (2020). Driver gaze estimation in the real world: Overcoming the eyeglass challenge. Proceedings of the 2020 IEEE Intelligent Vehicles Symposium (IV), IEEE.","DOI":"10.1109\/IV47402.2020.9304573"},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Krafka, K., Khosla, A., Kellnhofer, P., Kannan, H., Bhandarkar, S., Matusik, W., and Torralba, A. (2016). Eye tracking for everyone. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2016.239"},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"5259","DOI":"10.1109\/TIP.2020.2982828","article-title":"Gaze estimation by exploring two-eye asymmetry","volume":"29","author":"Cheng","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s42492-023-00135-6","article-title":"EM-Gaze: Eye context correlation and metric learning for gaze estimation","volume":"6","author":"Zhou","year":"2023","journal-title":"Vis. Comput. Ind. Biomed. Art"},{"key":"ref_122","first-page":"1","article-title":"I2DNet-Design and Real-Time Evaluation of Appearance-based gaze estimation system","volume":"14","author":"Murthy","year":"2021","journal-title":"J. Eye Mov. Res."},{"key":"ref_123","unstructured":"Palmero, C., Selva, J., Bagheri, M.A., and Escalera, S. (2018). Recurrent cnn for 3d gaze estimation using appearance and shape cues. arXiv."},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Dias, P.A., Malafronte, D., Medeiros, H., and Odone, F. (2020). Gaze estimation for assisted living environments. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, IEEE.","DOI":"10.1109\/WACV45572.2020.9093439"},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Jyoti, S., and Dhall, A. (2018). Automatic eye gaze estimation using geometric & texture-based networks. Proceedings of the 2018 24th International Conference on Pattern Recognition (ICPR), IEEE.","DOI":"10.1109\/ICPR.2018.8545162"},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/s12559-025-10452-y","article-title":"Free-Head Gaze Estimation with Deep Learning","volume":"17","author":"Duan","year":"2025","journal-title":"Cogn. Comput."},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Li, C., Li, F., Zhang, K., Chen, N., and Pan, Z. (2025). Gaze estimation network based on multi-head attention, fusion, and interaction. Sensors, 25.","DOI":"10.3390\/s25061893"},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Ogusu, R., and Yamanaka, T. (2019). LPM: Learnable pooling module for efficient full-face gaze estimation. Proceedings of the 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), IEEE.","DOI":"10.1109\/FG.2019.8756523"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.1109\/TCDS.2022.3210219","article-title":"A Complementary Dual-branch Network for Appearance-based Gaze Estimation from Low-resolution Facial Image","volume":"15","author":"Zhu","year":"2022","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"10982","DOI":"10.1007\/s10489-024-05778-3","article-title":"Fine-grained gaze estimation based on the combination of regression and classification losses: AA Abdelrahman et al","volume":"54","author":"Abdelrahman","year":"2024","journal-title":"Appl. Intell."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1007\/s00138-025-01690-z","article-title":"Mobgazenet: Robust gaze estimation mobile network based on progressive attention mechanisms","volume":"36","author":"Abdelrahman","year":"2025","journal-title":"Mach. Vis. Appl."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"890","DOI":"10.1109\/TCYB.2022.3156367","article-title":"Exploiting Cross-Modal Prediction and Relation Consistency for Semisupervised Image Captioning","volume":"54","author":"Yang","year":"2022","journal-title":"IEEE Trans. Cybern."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1016\/j.neucom.2022.01.005","article-title":"Review the state-of-the-art technologies of semantic segmentation based on deep learning","volume":"493","author":"Mo","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"4018","DOI":"10.1109\/TPAMI.2022.3217046","article-title":"Unsupervised domain adaptation of object detectors: A survey","volume":"46","author":"Oza","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.1007\/s11263-022-01739-w","article-title":"Vitaev2: Vision transformer advanced by exploring inductive bias for image recognition and beyond","volume":"131","author":"Zhang","year":"2023","journal-title":"Int. J. Comput. Vis."},{"key":"ref_136","first-page":"682","article-title":"Semi-supervised multi-modal clustering and classification with incomplete modalities","volume":"33","author":"Yang","year":"2019","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Kellnhofer, P., Recasens, A., Stent, S., Matusik, W., and Torralba, A. (2019). Gaze360: Physically unconstrained gaze estimation in the wild. Proceedings of the IEEE\/CVF International Conference on Computer Vision, IEEE.","DOI":"10.1109\/ICCV.2019.00701"},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"Zhang, X., Park, S., Beeler, T., Bradley, D., Tang, S., and Hilliges, O. (2020). ETH-XGaze: A large scale dataset for gaze estimation under extreme head pose and gaze variation. Proceedings of the European Conference on Computer Vision (ECCV), Springer.","DOI":"10.1007\/978-3-030-58558-7_22"},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"3010","DOI":"10.1109\/TNNLS.2018.2865525","article-title":"Multiview multitask gaze estimation with deep convolutional neural networks","volume":"30","author":"Lian","year":"2018","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"ref_140","doi-asserted-by":"crossref","unstructured":"Wang, K., Zhao, R., Su, H., and Ji, Q. (2019). Generalizing eye tracking with bayesian adversarial learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2019.01218"},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"Yu, Y., and Odobez, J.M. (2020). Unsupervised representation learning for gaze estimation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR42600.2020.00734"},{"key":"ref_142","doi-asserted-by":"crossref","unstructured":"Ghosh, S., Hayat, M., Dhall, A., and Knibbe, J. (2022). Mtgls: Multi-task gaze estimation with limited supervision. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, IEEE.","DOI":"10.1109\/WACV51458.2022.00123"},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"2539","DOI":"10.1038\/s41598-020-59251-5","article-title":"Gaze-in-wild: A dataset for studying eye and head coordination in everyday activities","volume":"10","author":"Kothari","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_144","doi-asserted-by":"crossref","unstructured":"Kothari, R., De Mello, S., Iqbal, U., Byeon, W., Park, S., and Kautz, J. (2021). Weakly-supervised physically unconstrained gaze estimation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR46437.2021.00985"},{"key":"ref_145","doi-asserted-by":"crossref","unstructured":"Palmero Cantarino, C., Komogortsev, O.V., and Talathi, S.S. (2020). Benefits of temporal information for appearance-based gaze estimation. Proceedings of the ACM Symposium on Eye Tracking Research and Applications, ACM.","DOI":"10.1145\/3379156.3391376"},{"key":"ref_146","first-page":"13127","article-title":"Self-learning transformations for improving gaze and head redirection","volume":"33","author":"Zheng","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_147","doi-asserted-by":"crossref","unstructured":"Chaudhary, A.K., Kothari, R., Acharya, M., Dangi, S., Nair, N., Bailey, R., Kanan, C., Diaz, G., and Pelz, J.B. (2019). Ritnet: Real-time semantic segmentation of the eye for gaze tracking. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW), IEEE.","DOI":"10.1109\/ICCVW.2019.00568"},{"key":"ref_148","doi-asserted-by":"crossref","unstructured":"Cheng, Y., and Lu, F. (2022). Gaze estimation using transformer. Proceedings of the 2022 26th International Conference on Pattern Recognition (ICPR), IEEE.","DOI":"10.1109\/ICPR56361.2022.9956687"},{"key":"ref_149","unstructured":"Cheng, Y., and Lu, F. (2021). Gaze estimation using transformer. arXiv."},{"key":"ref_150","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_151","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018). Shufflenet: An extremely efficient convolutional neural network for mobile devices. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref_152","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_153","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_154","doi-asserted-by":"crossref","unstructured":"Graham, B., El-Nouby, A., Touvron, H., Stock, P., Joulin, A., J\u00e9gou, H., and Douze, M. (2021). Levit: A vision transformer in convnet\u2019s clothing for faster inference. Proceedings of the IEEE\/CVF International Conference on Computer Vision, IEEE.","DOI":"10.1109\/ICCV48922.2021.01204"},{"key":"ref_155","unstructured":"Mehta, S., and Rastegari, M. (2021). Mobilevit: Light-weight, general-purpose, and mobile-friendly vision transformer. arXiv."},{"key":"ref_156","unstructured":"Mehta, S., and Rastegari, M. (2022). Separable self-attention for mobile vision transformers. arXiv."},{"key":"ref_157","unstructured":"Wadekar, S.N., and Chaurasia, A. (2022). Mobilevitv3: Mobile-friendly vision transformer with simple and effective fusion of local, global and input features. arXiv."},{"key":"ref_158","unstructured":"Xu, T., Wu, B., Fan, R., Zhou, Y., and Huang, D. (2023). FR-Net: A Light-weight FFT Residual Net For Gaze Estimation. arXiv."},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"111113","DOI":"10.1016\/j.engappai.2025.111113","article-title":"Towards fusing gaze estimation and object prediction: What are you looking at?","volume":"157","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_160","doi-asserted-by":"crossref","unstructured":"Zeng, H., Liu, W., Xia, T., Chen, J., Li, Z., and Zhang, S.Q. (2025). Foveated instance segmentation. Proceedings of the Computer Vision and Pattern Recognition Conference, IEEE.","DOI":"10.1109\/CVPR52734.2025.02281"},{"key":"ref_161","doi-asserted-by":"crossref","unstructured":"Smith, B.A., Yin, Q., Feiner, S.K., and Nayar, S.K. (2013). Gaze locking: Passive eye contact detection for human-object interaction. Proceedings of the 26th Annual ACM Symposium on User Interface Software and Technology, ACM.","DOI":"10.1145\/2501988.2501994"},{"key":"ref_162","doi-asserted-by":"crossref","unstructured":"Funes Mora, K.A., Monay, F., and Odobez, J.M. (2014). Eyediap: A database for the development and evaluation of gaze estimation algorithms from rgb and rgb-d cameras. Proceedings of the Symposium on Eye Tracking Research and Applications, ACM.","DOI":"10.1145\/2578153.2578190"},{"key":"ref_163","doi-asserted-by":"crossref","unstructured":"Cortacero, K., Fischer, T., and Demiris, Y. (2019). RT-BENE: A dataset and baselines for real-time blink estimation in natural environments. Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops, IEEE.","DOI":"10.1109\/ICCVW.2019.00147"},{"key":"ref_164","unstructured":"Park, S., Aksan, E., Zhang, X., and Hilliges, O. (2020). Towards end-to-end video-based eye-tracking. Proceedings of the Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, 23\u201328 August 2020, Proceedings, Part XII 16, Springer."},{"key":"ref_165","unstructured":"Recasens, A., Khosla, A., Vondrick, C., and Torralba, A. (2015). Where are they looking?. Advances in Neural Information Processing Systems 28, Curran Associates, Inc."},{"key":"ref_166","doi-asserted-by":"crossref","unstructured":"Wood, E., Baltru\u0161aitis, T., Morency, L.P., Robinson, P., and Bulling, A. (2016). Learning an appearance-based gaze estimator from one million synthesised images. Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications, ACM.","DOI":"10.1145\/2857491.2857492"},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"2194","DOI":"10.1109\/TIFS.2019.2959978","article-title":"Towards real-time eyeblink detection in the wild: Dataset, theory and practices","volume":"15","author":"Hu","year":"2019","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_168","doi-asserted-by":"crossref","unstructured":"Fan, L., Wang, W., Huang, S., Tang, X., and Zhu, S.C. (2019). Understanding human gaze communication by spatio-temporal graph reasoning. ETRA\u201916 Proceedings of the IEEE\/CVF International Conference on Computer Vision, ACM.","DOI":"10.1109\/ICCV.2019.00582"},{"key":"ref_169","doi-asserted-by":"crossref","unstructured":"Daza, R., Morales, A., Fierrez, J., and Tolosana, R. (2020). MEBAL: A multimodal database for eye blink detection and attention level estimation. ICMI\u201920 Proceedings of the Companion Publication of the 2020 International Conference on Multimodal Interaction, ACM.","DOI":"10.1145\/3395035.3425257"},{"key":"ref_170","doi-asserted-by":"crossref","unstructured":"Tomas, H., Reyes, M., Dionido, R., Ty, M., Mirando, J., Casimiro, J., Atienza, R., and Guinto, R. (2021). Goo: A dataset for gaze object prediction in retail environments. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, ACM.","DOI":"10.1109\/CVPRW53098.2021.00349"},{"key":"ref_171","doi-asserted-by":"crossref","unstructured":"Emery, K.J., Zannoli, M., Warren, J., Xiao, L., and Talathi, S.S. (2021). OpenNEEDS: A dataset of gaze, head, hand, and scene signals during exploration in open-ended VR environments. ETRA\u201921 Proceedings of the ACM Symposium on Eye Tracking Research and Applications, ACM.","DOI":"10.1145\/3448018.3457996"},{"key":"ref_172","doi-asserted-by":"crossref","unstructured":"Wang, Y., Jiang, Y., Li, J., Ni, B., Dai, W., Li, C., Xiong, H., and Li, T. (2022). Contrastive regression for domain adaptation on gaze estimation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recnognitio, ACM.","DOI":"10.1109\/CVPR52688.2022.01877"}],"container-title":["Robotics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2218-6581\/15\/4\/69\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T05:18:09Z","timestamp":1774588689000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2218-6581\/15\/4\/69"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,25]]},"references-count":172,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["robotics15040069"],"URL":"https:\/\/doi.org\/10.3390\/robotics15040069","relation":{},"ISSN":["2218-6581"],"issn-type":[{"value":"2218-6581","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,25]]}}}