Abstract
Over the last two decades, researchers have demonstrated the potentials in using eye gaze data for various tasks across fields of human endeavor. In this light, Remote Eye Gaze Tracking Systems (REGTs) rose to popularity with the presentation of several hardware and software modules intended for the Point of Gaze (POG) estimation task. This paper presents a paradigm shift in REGTs research from past to recent progress point of view. Our findings and discussions are focused on hardware, software, and application areas of REGTs, which we hope will benefit current and future researchers in this field.
Keywords
- Remote application
- Eye gaze tracking
- Setup
- Approach
- Algorithm
- Dataset
- Application area
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Abdrabou, Y., Khamis, M., Eisa, R.M., Ismail, S., Elmougy, A.: Just gaze and wave: exploring the use of gaze and gestures for shoulder-surfing resilient authentication. In: Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications (Denver, Colorado) (ETRA 2019), p. 10. Association for Computing Machinery, New York, Article 29 (2019). https://doi.org/10.1145/3314111.3319837
Akashi, T., Wakasa, Y., Tanaka, K., Karungaru, S., Fukumi, M.: Using genetic algorithm for eye detection and tracking in video sequence. J. Syst. Cybern. Inform. 5 (2007)
Al-Rahayfeh, A., Faezipour, M.: Eye tracking and head movement detection: a state-of-art survey. IEEE J. Transl. Eng. Health Med. 1(2013), 2100212 (2013). https://doi.org/10.1109/JTEHM.2013.2289879
Alioua, N., Amine, A., Rziza, M., Aboutajdine, D.: Eye state analysis using iris detection based on Circular Hough Transform. In: 2011 International Conference on Multimedia Computing and Systems, pp. 1–5 (2011). https://doi.org/10.1109/ICMCS.2011.5945576
Alnajar, F., Gevers, T., Valenti, R., Ghebreab, S.: Calibration-free gaze estimation using human gaze patterns. In: 2013 IEEE International Conference on Computer Vision, pp. 137–144 (2013)
Amarnag, S., Kumaran, R.S., Gowdy, J.N.: Real time eye tracking for human computer interfaces. In: 2003 International Conference on Multimedia and Expo. ICME 2003. Proceedings (Cat. No. 03TH8698), vol. 3, p. III–557 (2003). https://doi.org/10.1109/ICME.2003.1221372
Armstrong, T., Olatunji, B.O.: Eye tracking of attention in the affective disorders: a meta-analytic review and synthesis. Clin. Psychol. Rev. 32(8), 704–723 (2012). https://doi.org/10.1016/j.cpr.2012.09.004
Asteriadis, S., Soufleros, D., Karpouzis, K., Kollias, S.: A natural head pose and eye gaze dataset. In: Proceedings of the International Workshop on Affective-Aware Virtual Agents and Social Robots (Boston, Massachusetts) (AFFINE 2009), p. 4. Association for Computing Machinery, New York, Article 1. https://doi.org/10.1145/1655260.1655261
Baek, S., Choi, K., Ma, C., Kim, Y., Ko, S.: Eyeball model-based iris center localization for visible image-based eye-gaze tracking systems. IEEE Trans. Consumer Electron. 59(2), 415–421 (2013). https://doi.org/10.1109/TCE.2013.6531125
Baluja, S., Pomerleau, D.: Non-Intrusive Gaze Tracking Using Artificial Neural Networks. Technical Report, USA (1994). https://doi.org/10.5555/864994
Berkovsky, S., Taib, R., Koprinska, I., Wang, E., Zeng, Y., Li, J., Kleitman, S.: Detecting personality traits using eye-tracking data. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI 2019), p. 12. Association for Computing Machinery, New York, Article Paper 221. https://doi.org/10.1145/3290605.3300451
Blignaut, P.: Mapping the pupil-glint vector to gaze coordinates in a simple video-based eye tracker. J. Eye Movement Res. 7 (2014)
Bozkir, E., Günlü, O., Fuhl, W., Schaefer, R.F., Kasneci, E.: Differential Privacy for Eye Tracking with Temporal Correlations. ArXiv abs/2002.08972 (2020)
Bozkir, E., Ünal, A.B., Akgün, M., Kasneci, E., Pfeifer, N.: Privacy Preserving Gaze Estimation using Synthetic Images via a Randomized Encoding Based Framework. ArXiv abs/1911.07936 (2019)
Brunyé, T.T., Drew, T., Weaver, D.L. and Elmore, J.G.: A review of eye tracking for understanding and improving diagnostic interpretation. Cogn. Res.: Principles Implications 4(1), 7 (2019). https://doi.org/10.1186/s41235-019-0159-2
Cai, H., Yu, H., Zhou, X., Liu, H.: Robust gaze estimation via normalized iris center-eye corner vector. In: Kubota, N., Kiguchi, K., Liu, H., Obo, T. (eds.) Intelligent Robotics and Applications. ICIRA 2016. Lecture Notes in Computer Science, vol. 9834, pp. 300–309. https://doi.org/10.1007/978-3-319-43506-0_26
Calvi, C., Porta, M., Sacchi, D.: e5Learning, an E-learning environment based on eye tracking. In: 2008 Eighth IEEE International Conference on Advanced Learning Technologies, pp. 376–380. https://doi.org/10.1109/ICALT.2008.35
Camgaze. [n.d.]. https://github.com/wallarelvo/camgaze. Accessed 4 Feb 4 2020
Carlin, J.D., Calder, A.J.: The neural basis of eye gaze processing. Curr. Opin. Neurobiol. 23(3), 450–455 (2013). https://doi.org/10.1016/j.conb.2012.11.014
Cerrolaza, J., Villanueva, A., Cabeza, R.: Taxonomic study of polynomial regressions applied to the calibration of video-oculographic systems. In: Eye Tracking Research and Applications Symposium (ETRA), pp. 259–266. https://doi.org/10.1145/1344471.1344530
Chen, M., Chen, Y., Yao, Z., Chen, W., Lu, Y.: Research on eye-gaze tracking network generated by augmented reality application. In: 2009 Second International Workshop on Knowledge Discovery and Data Mining, pp. 594–597 (2009). https://doi.org/10.1109/WKDD.2009.73
Cheng, H., Liu, Y., Fu, W., Ji, Y., Yang, L., Zhao, Y., Yang, J.: Gazing point dependent eye gaze estimation. Pattern Recogn. 71 (2017). https://doi.org/10.1016/j.patcog.2017.04.026
Cheng, Y., Lu, F., Zhang, X.: Appearance-based gaze estimation via evaluation-guided asymmetric regression. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018, pp. 105–121. Springer, Cham. https://doi.org/10.1007/978-3-030-01264-9_7
Cherif, Z.R., Nait-Ali, A., Motsch, J.F., Krebs, M.O.: An adaptive calibration of an infrared light device used for gaze tracking. In: IMTC/2002. Proceedings of the 19th IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No. 00CH37276), vol. 2, pp. 1029–1033 (2002)
Cheung, Y., Peng, Q.: Eye gaze tracking with a web camera in a desktop environment. IEEE Trans. Hum.-Mach. Syst. 45(4), 419–430 (2015). https://doi.org/10.1109/THMS.2015.2400442
Chi, J.-N., Zhang, C., Yan, Y.-T., Liu, Y., Zhang, H.: Eye Gaze Calculation Based on Nonlinear Polynomial and Generalized Regression Neural Network, vol. 3, pp. 617–623. https://doi.org/10.1109/ICNC.2009.599
Cho, D.-C., Kim, W.-Y.: Long-range gaze tracking system for large movements. IEEE Trans. Bio-med. Eng. 60 (2013). https://doi.org/10.1109/TBME.2013.2266413
Cho, S.W., Baek, N.R., Kim, M.C., Koo, J.H., Kim, J.H., Park, K.R.: Face detection in nighttime images using visible-light camera sensors with two-step faster region-based convolutional neural network. Sensors 18, 9 (2018). https://doi.org/10.3390/s18092995
Coetzer, R.C., Hancke, G.P.: Eye detection for a real-time vehicle driver fatigue monitoring system. In: 2011 IEEE Intelligent Vehicles Symposium (IV), pp. 66–71 (2011). https://doi.org/10.1109/IVS.2011.5940406
Cortacero, K., Fischer, T., Demiris, Y.: RT-BENE: a dataset and baselines for real-time blink estimation in natural environments. In: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 1159–1168 (2019). https://doi.org/10.1109/ICCVW.2019.00147
Coutinho, F.L., Morimoto, C.H.: Free head motion eye gaze tracking using a single camera and multiple light sources. In: 2006 19th Brazilian Symposium on Computer Graphics and Image Processing, pp. 171–178 (2006). https://doi.org/10.1109/SIBGRAPI.2006.21
Danforth, R., Duchowski, A., Geist, R., Mcaliley, E.: A platform for gaze-contingent virtual environments. In: Smart Graphics (2000 AAAI Spring Symposium, Technical Report SS-00-04), (Menlo Park, CA, 2000), pp. 66–70. AAAI (2000)
De Luca, A., Denzel, M., Hussmann, H.: Look into my eyes! can you guess my password? In: Proceedings of the 5th Symposium on Usable Privacy and Security (Mountain View, California, USA) (SOUPS 2009), p. 12. Association for Computing Machinery, New York, Article 7 (2009). https://doi.org/10.1145/1572532.1572542
De Luca, A., Weiss, R., Drewes, H.: Evaluation of eye-gaze interaction methods for security enhanced PIN-entry. In: Proceedings of the 19th Australasian Conference on Computer-Human Interaction: Entertaining User Interfaces (Adelaide, Australia) (OZCHI 2007), pp. 199–202. Association for Computing Machinery, New York (2007). https://doi.org/10.1145/1324892.1324932
Domdei, N., Linden, M., Reiniger, J.L., Holz, F.G., Harmening, W.M.: Eye tracking-based estimation and compensation of chromatic offsets for multi-wavelength retinal microstimulation with foveal cone precision. Biomed. Opt. Express 10(8), 4126–4141 (2019). https://doi.org/10.1364/BOE.10.004126
Yoo, D.H., Chung, M.J.: Non-intrusive eye gaze estimation without knowledge of eye pose. In: Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 785–790 (2004). https://doi.org/10.1109/AFGR.2004.1301630
Drewes, H., De Luca, A., Schmidt, A.: Eye-gaze interaction for mobile phones. In Proceedings of the 4th International Conference on Mobile Technology, Applications, and Systems and the 1st International Symposium on Computer Human Interaction in Mobile Technology (Singapore) (Mobility 2007), pp. 364–371. Association for Computing Machinery, New York (2007). https://doi.org/10.1145/1378063.1378122
Duchowski, A.: Eye Tracking Methodology: Theory and Practice, 2 edn. Springer, London (2007). https://doi.org/10.1007/978-1-84628-609-4
Duchowski, A.T.: A breadth-first survey of eye-tracking applications. Behav. Res. Methods Instrum. Comput. 34(4), 455–470 (2002). https://doi.org/10.3758/BF03195475
Ebisawa, Y., Satoh, S.: Effectiveness of pupil area detection technique using two light sources and image difference method. In: Proceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1268–1269 (1993). https://doi.org/10.1109/IEMBS.1993.979129
Eggert, T.: Eye movement recordings: methods. Dev. Ophthamol. 40(2007), 15–34 (2007). https://doi.org/10.1159/000100347
Eibenberger, K., Eibenberger, B., Roberts, D.C., Haslwanter, T., Carey, J.P.: A novel and inexpensive digital system for eye movement recordings using magnetic scleral search coils. Med. Biol. Eng. Comput. 54(2016), 421–430 (2016). https://doi.org/10.1007/s11517-015-1326-3
CVC ET. [n.d.]. https://github.com/tiendan/. Accessed 3 Feb 2020
EyeLink. [n.d.]. http://www.eyelinkinfo.com/. Accessed 3 Mar 2020
EyeTab. [n.d.]. https://github.com/errollw/EyeTab. Accessed 4 Feb 2020
Bryn Farnsworth. 2019. 10 Free Eye Tracking Software Programs [Pros and Cons]. https://imotions.com/blog/free-eye-tracking-software/. Accessed 5 Mar 2019
Bryn Farnsworth. 2020. The iMotions Screen-Based Eye Tracking Module [Explained]. https://imotions.com/blog/screen-based-eye-tracking-module/. Accessed 5 Feb 2020
Bryn Farnsworth. Top 12 Eye Tracking Hardware Companies (2020). https://imotions.com/blog/top-eyetracking-hardware-companies/. Accessed 3 Mar 2020
Ferhat, O., Vilariño, F.: Low cost eye tracking: the current panorama. Comput. Intell. Neurosci. 1–14 (2016). https://doi.org/10.1155/2016/8680541
Ferhat, O., Vilariño, F., Sánchez, F.J.: A cheap portable eye-tracker solution for common setups. J. Eye Movement Res. 7 (2014)
Fischer, T., Chang, H.J., Demiris, Y.: RT-GENE: real-time eye gaze estimation in natural environments. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018, pp. 339–357. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01249-6_21
Fookes, C., Maeder, A., Sridharan, S., Mamic, G.: Gaze based personal identification. In: Wang, L., Geng, X. (eds.) Behavioural Biometrics for Human Identification: Intelligent Applications, pp. 237–263. IGI Global, United States (2010). https://doi.org/10.4018/978-1-60566-725-6.ch012
Fu, B., Yang, R.: Display control based on eye gaze estimation. In: 2011 4th International Congress on Image and Signal Processing, vol. 1, pp. 399–403 (2011). https://doi.org/10.1109/CISP.2011.6099973
Fu, X., Guan, X., Peli, E., Liu, H., Luo, G.: Automatic calibration method for driver’s head orientation in natural driving environment. IEEE Trans. Intell. Transp. Syst. 14(1), 303–312 (2013). https://doi.org/10.1109/TITS.2012.2217377
Fu, Y., Zhu, W., Massicotte, D.: A gaze tracking scheme with low resolution image. In: 2013 IEEE 11th International New Circuits and Systems Conference (NEWCAS), pp. 1–4 (2013). https://doi.org/10.1109/NEWCAS.2013.6573660
Mora, K.A.F., Monay, F., Odobez, J.-M.: EYEDIAP: a database for the development and evaluation of gaze estimation algorithms from RGB and RGB-D cameras. In: Proceedings of the Symposium on Eye Tracking Research and Applications (Safety Harbor, Florida) (ETRA 2014), pp. 255–258. Association for Computing Machinery, New York (2014). https://doi.org/10.1145/2578153.2578190
Funes Mora, K.A., Odobez, J.: Geometric generative gaze estimation (G3E) for remote RGB-D cameras. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1773–1780 (2014). https://doi.org/10.1109/CVPR.2014.229
Gatys, L., Ecker, A., Bethge, M.: A Neural Algorithm of Artistic Style. arXiv (2015). https://doi.org/10.1167/16.12.326
GazeParser. [n.d.]. http://gazeparser.sourceforge.net/. Accessed 5 Feb 2020
Gazepointer. [n.d.]. https://sourceforge.net/projects/gazepointer/. Accessed 5 Feb 2020
Genco, S.: What Eye-Tracking Can and Can’t Tell You About Attention (2019). https://www.nmsba.com/buying-neuromarketing/neuromarketing-techniques/what-eye-tracking-can-and-cant-tell-you-about-attention. Accessed 7 Oct 2019
Demiris, Y., Georgiou, T.: Adaptive user modelling in car racing games using behavioural and physiological data. User Modeling User-Adapted Interaction 27(2), 267–311 (2017). https://doi.org/10.1007/s11257-017-9192-3
Glenstrup, A., Engell-Nielsen, T.: Eye controlled media: present and future state. Master’s thesis. University of Copenhagen DIKU (Institute of Computer Science), Denmark (1995)
Guestrin, E.D., Eizenman, M.: General theory of remote gaze estimation using the pupil center and corneal reflections. IEEE Trans. Biomed. Eng. 53(6), 1124–1133 (2006). https://doi.org/10.1109/TBME.2005.863952
Guo, Z., Qianxiang, Z., Liu, Z.: Appearance-based gaze estimation under slight head motion. Multimedia Tools Appl. 76 (2016). https://doi.org/10.1007/s11042-015-3182-4
Wu, H., Chen, Q., Wada, T.: Conic-based algorithm for visual line estimation from one image. In: Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 260–265 (2004)
Hansen, D., Nielsen, M., Hansen, J., Johansen, A., Stegmann, M.: Tracking eyes using shape and appearance. In: IAPR Workshop on Machine Vision Applications, pp. 201–204 (2002)
Hansen, D.W., Hansen, J.P., Nielsen, M., Johansen, A.S., Stegmann, M.B.: Eye typing using Markov and active appearance models. In: Sixth IEEE Workshop on Applications of Computer Vision, 2002. (WACV 2002), pp. 132–136 (2002)
Hansen, D.W., Ji, Q.: In the eye of the beholder: a survey of models for eyes and gaze. IEEE Trans. Pattern Anal. Mach. Intell. 32(3), 478–500 (2010). https://doi.org/10.1109/TPAMI.2009.30
Hansen, D.W., Pece, A.: Eye typing off the shelf. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, vol. 2, p. II (2004)
Haro, A., Flickner, M., Essa, I.: Detecting and tracking eyes by using their physiological properties, dynamics, and appearance. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No. PR00662), vol. 1, pp. 163–168 (2000). https://doi.org/10.1109/CVPR.2000.855815
Hayhoe, M.M., Matthis, J.S.: Control of gaze in natural environments: effects of rewards and costs, uncertainty and memory in target selection. Interface Focus 8(4), 20180009 (2018). https://doi.org/10.1098/rsfs.2018.0009, arXiv:https://royalsocietypublishing.org/doi/pdf/10.1098/rsfs.2018.0009
He, Q., Hong, X., Chai, X., Holappa, J., Zhao, G., Chen, X., Pietikäinen, M.: OMEG: oulu multi-pose eye gaze dataset. In: Paulsen, R.R., Pedersen, K.S. (eds.) Image Analysis, pp. 418–427. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19665-7_35
Heidenburg, B., Lenisa, M., Wentzel, D., Malinowski, A.: Data mining for gaze tracking system. In: 2008 Conference on Human System Interactions, pp. 680–683 (2008). https://doi.org/10.1109/HSI.2008.4581522
Hennessey, C., Noureddin, B., Lawrence, P.: A single camera eye-gaze tracking system with free head motion. In: Proceedings of the 2006 Symposium on Eye Tracking Research & Applications (San Diego, California) (ETRA 2006), pp. 87–94. Association for Computing Machinery, New York (2006). https://doi.org/10.1145/1117309.1117349
Fiset Jacob Hennessey, C.: Long range eye tracking: bringing eye tracking into the living room. In: Proceedings of the 2012 Symposium on Eye Tracking Research and Applications, pp. 249–252 (2012). https://doi.org/10.1145/2168556.2168608
Yamazoe, H., Utsumi, A., Yonezawa, T., Abe, S.: Remote and head-motion-free gaze tracking for real environments with automated head-eye model calibrations. In: 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 1–6 (2008)
Huang, Q., Veeraraghavan, A., Sabharwal, A.: TabletGaze: dataset and analysis for unconstrained appearance based gaze estimation in mobile tablets. Mach. Vis. Appl. 28 (2017). https://doi.org/10.1007/s00138-017-0852-4
Huang, S., Wu, Y., Hung, W., Tang, C.: Point-of-regard measurement via iris contour with one eye from single image. In: 2010 IEEE International Symposium on Multimedia, pp. 336–341 (2010)
Huang, Y., Dong, X., Hao, M.: Eye gaze calibration based on support vector regression machine, 454–456 (2011). https://doi.org/10.1109/WCICA.2011.5970555
imotions. 2015. Top 8 Eye Tracking Applications in Research. https://imotions.com/blog/top-8-applications-eye-tracking-research/. Accessed 16 Feb 2020
ITU. [n.d.]. https://github.com/devinbarry/GazeTracker. Accessed 5 Feb 2020
Jafari, R., Ziou, D.: Gaze estimation using Kinect/PTZ camera. In: 2012 IEEE International Symposium on Robotic and Sensors Environments Proceedings, pp. 13–18 (2012). https://doi.org/10.1109/ROSE.2012.6402633
Wang, J.-G., Sung, E.: Study on eye gaze estimation. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 32(3), 332–350 (2002). https://doi.org/10.1109/TSMCB.2002.999809
Jian-nan, C., Peng-yi, Z., Si-yi, Z., Chuang, Z., Ying, H.: Key techniques of eye gaze tracking based on pupil corneal reflection. In: 2009 WRI Global Congress on Intelligent Systems, vol. 2, pp. 133–138 (2009)
Jiaqi, J., Zhou, X., Chan, S., Chen, S.: Appearance-Based Gaze Tracking: A Brief Review, 629–640 (2019). https://doi.org/10.1007/978-3-030-27529-7_53
Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: European Conference on Computer Vision (2016)
Jording, M., Engemann, D., Eckert, H., Bente, G., Vogeley, K.: Distinguishing social from private intentions through the passive observation of gaze cues. Front. Hum. Neurosci. 13(2019), 442 (2019). https://doi.org/10.3389/fnhum.2019.00442
Fujitsu Journal. [n.d.]. Gaze Tracking Technology - the Possibilities and Future. http://journal.jp.fujitsu.com/en/2014/09/09/01/. Accessed 17 Sept 2019
Joyce, C.A., Gorodnitsky, I.F., King, J.W., Kutas, M.: Tracking eye fixations with electroocular and electroencephalographic recordings. Psychophysiology 39(5), 607–618 (2002). https://doi.org/10.1017/S0048577202394113
Hansen, J.P., Mardanbegi, D., Biermann, F., Bækgaard, P.: A gaze interactive assembly instruction with pupillometric recording. Behav. Res. Methods 50(4), 1723–1733 (2018). https://doi.org/10.3758/s13428-018-1074-z
Juhong, A., Treebupachatsakul, T., Pintavirooj, C.: Smart eye-tracking system. In: 2018 International Workshop on Advanced Image Technology (IWAIT), pp. 1–4 (2018). https://doi.org/10.1109/IWAIT.2018.8369701
Rayner, K.: Eye movements in reading and information processing: 20 years of research. Psychol. Bull. 124(3), 372–422 (1998). https://doi.org/10.1037/0033-2909.124.3.372
Kaminski, J.Y., Knaan, D., Shavit, A.: Single image face orientation and gaze detection. Mach. Vis. Appl. 21(1), 85 (2008). https://doi.org/10.1007/s00138-008-0143-1
Kanowski, M., Rieger, J.W., Noesselt, T., Tempelmann, C., Hinrichs, H.: Endoscopic eye tracking system for fMRI. J. Neurosci. Methods 160(1), 10–15 (2007). https://doi.org/10.1016/j.jneumeth.2006.08.001
Kar, A., Corcoran, P.: A review and analysis of eye-gaze estimation systems, algorithms and performance evaluation methods in consumer platforms. IEEE Access 5(2017), 16495–16519 (2017). https://doi.org/10.1109/ACCESS.2017.2735633
Tan, K.-H., Kriegman, D.J., Ahuja, N.: Appearance-based eye gaze estimation. In: Sixth IEEE Workshop on Applications of Computer Vision, 2002. (WACV 2002), pp. 191–195 (2002)
Kawato, S., Tetsutani, N.: Detection and tracking of eyes for gaze-camera control. Image Vis. Comput. 22(12), 1031–1038 (2004). https://doi.org/10.1016/j.imavis.2004.03.013. Proceedings from the 15th International Conference on Vision Interface
Khamis, M., Alt, F., Hassib, M., von Zezschwitz, E., Hasholzner, R., Bulling, A.: GazeTouchPass: multimodal authentication using gaze and touch on mobile devices. In: Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems (San Jose, California, USA) (CHI EA 2016), pp. 2156–2164. Association for Computing Machinery, New York (2016). https://doi.org/10.1145/2851581.2892314
Khamis, M., Hasholzner, R., Bulling, A., Alt, F.: GTmoPass: two-factor authentication on public displays using gaze-touch passwords and personal mobile devices. In: Proceedings of the 6th ACM International Symposium on Pervasive Displays (Lugano, Switzerland) (PerDis 2017), p. 9. Association for Computing Machinery, New York, Article 8. https://doi.org/10.1145/3078810.3078815
Khamis, M., Hassib, M., von Zezschwitz, E., Bulling, A., Alt, F.: GazeTouchPIN: protecting sensitive data on mobile devices using secure multimodal authentication. In: Proceedings of the 19th ACM International Conference on Multimodal Interaction (Glasgow, UK) (ICMI 2017), pp. 446–450. Association for Computing Machinery, New York. https://doi.org/10.1145/3136755.3136809
Ki, J., Kwon, Y.: 3D gaze estimation and interaction. In: 2008 3DTV Conference: the True Vision - Capture, Transmission and Display of 3D Video, pp. 373–376 (2008). https://doi.org/10.1109/3DTV.2008.4547886
Kim, S.M., Sked, M., Ji, Q.: Non-intrusive eye gaze tracking under natural head movements. In: The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 1, pp. 2271–2274 (2004). https://doi.org/10.1109/IEMBS.2004.1403660
Klaib, A., Alsrehin, N., Melhem, W., Bashtawi, H.: IoT smart home using eye tracking and voice interfaces for elderly and special needs people. J. Commun. 614–621 (2019). https://doi.org/10.12720/jcm.14.7.614-621
Kocejko, T., Bujnowski, A., Wtorek, J.: Eye mouse for disabled. In: 2008 Conference on Human System Interactions, pp. 199–202 (2008). https://doi.org/10.1109/HSI.2008.4581433
Kotus, J., Kunka, B., Czyzewski, A., Szczuko, P., Dalka, P., Rybacki, R.: Gaze-tracking and Acoustic Vector Sensors Technologies for PTZ Camera Steering and Acoustic Event Detection (2010). https://doi.org/10.1109/DEXA.2010.62
Krafka, K., Khosla, A., Kellnhofer, P., Kannan, H., Bhandarkar, S., Matusik, W., Torralba, A.: Eye tracking for everyone. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2176–2184. https://doi.org/10.1109/CVPR.2016.239
Kumar, M., Garfinkel, T., Boneh, D., Winograd, T.: Reducing shoulder-surfing by using gaze-based password entry. In: Proceedings of the 3rd Symposium on Usable Privacy and Security (Pittsburgh, Pennsylvania, USA) (SOUPS 2007), pp. 13–19. Association for Computing Machinery, New York. https://doi.org/10.1145/1280680.1280683
Lallé, S., Conati, C., Carenini, G.: Predicting confusion in information visualization from eye tracking and interaction data. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (New York, New York, USA) (IJCAI 2016), pp. 2529–2535. AAAI Press (2016)
Lee, J.W., Cho, C.W., Shin, K.Y., Lee, E.C., Park, K.R.: 3D gaze tracking method using Purkinje images on eye optical model and pupil. Opt. Lasers Eng. 50(5), 736–751 (2012). https://doi.org/10.1016/j.optlaseng.2011.12.001
Lee, W.O., Cho, C.W., Gwon, S.Y., Park, K.R., Lee, H., Cha, J., Lee, H.C.: Remote gaze tracking system on a large display. Sensors 13(10), 13439–13463 (2013). https://doi.org/10.3390/s131013439
Lee, S.J., Jo, J., Jung, H.G., Park, K.R., Kim, J.: Real-time gaze estimator based on driver’s head orientation for forward collision warning system. IEEE Trans. Intell. Transp. Syst. 12(1), 254–267 (2011). https://doi.org/10.1109/TITS.2010.2091503
Li, Y., Monaghan, D.S., O’Connor, N.E.: Real-time gaze estimation using a kinect and a HD webcam. In: Gurrin, C., Hopfgartner, F., Hurst, W., Johansen, H., Lee, H., O’Connor, N. (eds.) MultiMedia Modeling, pp. 506–517. Springer, Cham (2014). . https://doi.org/10.1007/978-3-319-04114-8_43
Lindén, E., Sjöstrand, J., Proutiere, A.: Learning to personalize in appearance-based gaze tracking. In: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 1140–1148 (2019). https://doi.org/10.1109/ICCVW.2019.00145
Liu, A., Xia, L., Duchowski, A., Bailey, R., Holmqvist, K., Jain, E.: Differential privacy for eye-tracking data. In: Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications (Denver, Colorado) (ETRA 2019), p. 10. Association for Computing Machinery, New York, Article 28 (2019). https://doi.org/10.1145/3314111.3319823
Liu, G., Yu, Y., Funes Mora, K.A., Odobez, J.: A differential approach for gaze estimation. IEEE Trans. Pattern Anal. Mach. Intell. 2019, 1–1 (2019). https://doi.org/10.1109/TPAMI.2019.2957373
Long, X., Tonguz, O.K., Kiderman, A.: A high speed eye tracking system with robust pupil center estimation algorithm. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3331–3334 (2007). https://doi.org/10.1109/IEMBS.2007.4353043
Lu, F., Okabe, T., Sugano, Y., Sato, Y.: Learning gaze biases with head motion for head pose-free gaze estimation. Image Vis. Comput. 32 (2014). https://doi.org/10.1016/j.imavis.2014.01.005
Lu, F., Sugano, Y., Okabe, T., Sato, Y.: Head pose-free appearance-based gaze sensing via eye image synthesis. In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR 2012), pp. 1008–1011 (2012)
Lu, F., Sugano, Y., Okabe, T., Sato, Y.: Gaze estimation from eye appearance: a head pose-free method via eye image synthesis. IEEE Trans. Image Process. 24(11), 3680–3693 (2015)
Lukander, K.: Measuring gaze point on handheld mobile devices. In: CHI 2004 Extended Abstracts on Human Factors in Computing Systems (Vienna, Austria) (CHI EA 2004), p. 1556. Association for Computing Machinery, New York (2004). https://doi.org/10.1145/985921.986132
Lupu, R.G., Ungureanu, F.: A survey of eye tracking methods and applications (2014)
Majaranta, P., Räihä, K.-J.: Twenty years of eye typing: systems and design issues. In: Eye Tracking Research and Applications Symposium (ETRA) 2002, pp. 15–22 (2002). https://doi.org/10.1145/507072.507076
Mansouryar, M., Steil, J., Sugano, Y., Bulling, A.: 3D gaze estimation from 2D pupil positions on monocular head-mounted eye trackers. In: Proceedings of the 9th ACM International Symposium on Eye Tracking Research & Applications (ETRA 2016), pp. 197–200 (2016). https://doi.org/10.1145/2857491.2857530
Martinez, F., Carbone, A., Pissaloux, E.: Gaze estimation using local features and non-linear regression. In: 2012 19th IEEE International Conference on Image Processing, pp. 1961–1964 (2012)
Massé, B., Ba, S., Horaud, R.: Tracking gaze and visual focus of attention of people involved in social interaction. IEEE Trans. Pattern Anal. Mach. Intell. 40(11), 2711–2724 (2018). https://doi.org/10.1109/TPAMI.2017.2782819
Mathis, F., Vaniea, K., Williamson, J., Khamis, M.: RubikAuth: fast and secure authentication in virtual reality. In: Proceedings of the ACM CHI Conference on Human Factors in Computing Systems 2020. Association for Computing Machinery (ACM), United States (2020)
Matsuno, S., Sorao, S., Susumu, C., Akehi, K., Itakura, N., Mizuno, T., Mito, K.: Eye-movement measurement for operating a smart device: a small-screen line-of-sight input system. In: 2016 IEEE Region 10 Conference (TENCON), pp. 3798–3800. https://doi.org/10.1109/TENCON.2016.7848773
Maurage, P., Masson, N., Bollen, Z., D’Hondt, F.: Eye tracking correlates of acute alcohol consumption: a systematic and critical review. Neurosci. Biobehav. Rev. 108, 400–422 (2020). https://doi.org/10.1016/j.neubiorev.2019.10.001
Metsis, V., Kosmopoulos, D., McMurrough, C.D.: A dataset for point of gaze detection using head poses and eye images. J Multimodal User Interfaces 7(2013), 207–215 (2013). https://doi.org/10.1007/s12193-013-0121-4
Meyer, A., Böhme, M., Martinetz, T., Barth, E.: A Single-camera remote eye tracker. In: André, E., Dybkjær, L., Minker, W., Neumann, H., Weber, M. (eds.) Perception and Interactive Technologies, pp. 208–211. Springer, Heidelberg (2006). https://doi.org/10.1007/11768029_25
Model, D., Eizenman, M.: User-calibration-free remote eye-gaze tracking system with extended tracking range. In: 2011 24th Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 001268–001271 (2011). https://doi.org/10.1109/CCECE.2011.6030667
Morimoto, C.H., Koons, D., Amir, A., Flickner, M.: Pupil detection and tracking using multiple light sources. Image Vis. Comput. 18(4), 331–335 (2000). https://doi.org/10.1016/S0262-8856(99)00053-0
Morimoto, C.H., Amir, A., Flickner, M.: Detecting eye position and gaze from a single camera and 2 light sources. In: Object Recognition Supported by User Interaction for Service Robots, vol. 4, pp. 314–317. https://doi.org/10.1109/ICPR.2002.1047459
Morimoto, C.H., Mimica, M.R.M.: Eye gaze tracking techniques for interactive applications. 98(1), 4–24 (2005). https://doi.org/10.1016/j.cviu.2004.07.010
Murphy-Chutorian, E., Doshi, A., Trivedi, M.M.: Head pose estimation for driver assistance systems: a robust algorithm and experimental evaluation. In: 2007 IEEE Intelligent Transportation Systems Conference, pp. 709–714 (2007). https://doi.org/10.1109/ITSC.2007.4357803
MyEye. [n.d.]. https://myeye.jimdofree.com/. Accessed 5 Feb 2020
Ramanauskas, N.: Calibration of video-oculographical eye tracking system. Electron. Electr. Eng. 8(72), 65–68 (2006)
NetGazer. [n.d.]. http://sourceforge.net/projects/netgazer/. Accessed 3 Feb 2020
Nguyen, P., Fleureau, J., Chamaret, C., Guillotel, P.: Calibration-free gaze tracking using particle filter. In: 2013 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2013)
Nilsson, S.: Interaction without gesture or speech – a gaze controlled AR system. In: 17th International Conference on Artificial Reality and Telexistence (ICAT 2007), pp. 280–281 (2007). https://doi.org/10.1109/ICAT.2007.43
NNET. [n.d.]. https://userweb.cs.txstate.edu/~ok11/nnet.html. Accessed 4 Feb 2020
Oeltermann, A., Ku, S.-P., Logothetis, N.K.: A novel functional magnetic resonance imaging compatible search-coil eye-tracking system. Magn. Reson. Imaging 25(6), 913–922 (2007). https://doi.org/10.1016/j.mri.2007.02.019
Ogama. [n.d.]. http://www.ogama.net/. Accessed 5 Feb 2020
Ohno, T., Mukawa, N.: A free-head, simple calibration, gaze tracking system that enables gaze-based interaction. In: Proceedings of the 2004 Symposium on Eye Tracking Research & Applications (San Antonio, Texas) (ETRA 2004), pp. 115–122. Association for Computing Machinery, New York. https://doi.org/10.1145/968363.968387
Ohno, T., Mukawa, N., Kawato, S.: Just blink your eyes: a head-free gaze tracking system. In: CHI 2003 Extended Abstracts on Human Factors in Computing Systems (Ft. Lauderdale, Florida, USA) (CHI EA 2003), pp. 950–957. Association for Computing Machinery, New York. https://doi.org/10.1145/765891.766088
Ohno, T., Mukawa, N., Yoshikawa, A.: FreeGaze: a gaze tracking system for everyday gaze interaction. In: Duchowski, A.T., Vertegaal, R., Senders, J.W. (eds.) Proceedings of the Eye Tracking Research & Application Symposium, ETRA 2002, New Orleans, Louisiana, USA, March 25-27, 2002, pp. 125–132. ACM. https://doi.org/10.1145/507072.507098
openEyes. [n.d.]. http://thirtysixthspan.com/openEyes/software.html. Accessed 5 Feb 2020
Opengazer. [n.d.]. http://www.inference.phy.cam.ac.uk/opengazer/. Accessed 3 Feb 2020
Palinko, O., Sciutti, A., Wakita, Y., Matsumoto, Y., Sandini, G.: If looks could kill: Humanoid robots play a gaze-based social game with humans, pp. 905–910 (2016). https://doi.org/10.1109/HUMANOIDS.2016.7803380
Papageorgiou, E., Hardiess, G., Mallot, H.A., Schiefer, U.: Gaze patterns predicting successful collision avoidance in patients with homonymous visual field defects. Vis. Res. 65(2012), 25–37 (2012). https://doi.org/10.1016/j.visres.2012.06.004
Park, S., Mello, S.D., Molchanov, P., Iqbal, U., Hilliges, O., Kautz, J.: Few-shot adaptive gaze estimation. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 9367–9376 (2019). https://doi.org/10.1109/ICCV.2019.00946
Park, S., Zhang, X., Bulling, A., Hilliges, O.: Learning to find eye region landmarks for remote gaze estimation in unconstrained settings. In: Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications (Warsaw, Poland) (ETRA 2018), p. 10. Association for Computing Machinery, New York, Article 21. https://doi.org/10.1145/3204493.3204545
Park, S.H., Yoon, H.S., Park, K.R.: Faster R-CNN and geometric transformation-based detection of driver’s eyes using multiple near-infrared camera sensors. Sensors 19, 1 (2019). https://doi.org/10.3390/s19010197
Patil, S.T., Meshram, M., Rahangdale, C., Shivhare, P., Jindal, L.: Eye gaze detection technique to interact with computer. Int. J. Eng. Res. Comput. Sci. Eng. (IJERCSE) 2(3), 92–96 (2015)
Pichitwong, W., Chamnongthai, K.: 3-D gaze estimation by stereo gaze direction. In: 2016 13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), pp. 1–4. https://doi.org/10.1109/ECTICon.2016.7561491
Pompe, M.T., Liasis, A., Hertle, R.: Visual electrodiagnostics and eye movement recording - World Society of Pediatric Ophthalmology and Strabismus (WSPOS) consensus statement. Indian J. Ophthalmol. 67(1), 23–30 (2019). https://doi.org/10.4103/ijo.IJO_1103_18
Ponz, V., Villanueva, A., Cabeza, R.: Dataset for the evaluation of eye detector for gaze estimation. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing (Pittsburgh, Pennsylvania) (UbiComp 2012), pp. 681–684. Association for Computing Machinery, New York (2012). https://doi.org/10.1145/2370216.2370364
Porta, M., Ricotti, S., Perez, C.J.: Emotional e-learning through eye tracking. In: Proceedings of the 2012 IEEE Global Engineering Education Conference (EDUCON), pp. 1–6 (2012). https://doi.org/10.1109/EDUCON.2012.6201145
Porta, S., Bossavit, B., Cabeza, R., Larumbe-Bergera, A., Garde, G., Villanueva, A.: U2Eyes: a binocular dataset for eye tracking and gaze estimation. In: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 3660–3664 (2019). https://doi.org/10.1109/ICCVW.2019.00451
Tobii proX. [n.d.]. How to position participants and the eye tracker. https://www.tobiipro.com/learnand-support/learn/steps-in-an-eye-tracking-study/run/how-to-position-the-participant-and-the-eye-tracker/. Accessed 26 Dec 2019
Pygaze. [n.d.]. http://www.pygaze.org/. Accessed 5 Feb 2020
Rajashekar, U., van der Linde, I., Bovik, A.C., Cormack, L.K.: GAFFE: a gaze-attentive fixation finding engine. IEEE Trans. Image Process. 17(4), 564–573 (2008). https://doi.org/10.1109/TIP.2008.917218
Rasouli, A., Kotseruba, I., Tsotsos, J.K.: Agreeing to cross: how drivers and pedestrians communicate. In: 2017 IEEE Intelligent Vehicles Symposium (IV), pp. 264–269 (2017). https://doi.org/10.1109/IVS.2017.7995730
Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6517–6525 (2017). https://doi.org/10.1109/CVPR.2017.690
Redmon, J., Farhadi, A.: YOLOv3: An Incremental Improvement. CoRR abs/1804.02767 (2018). arXiv:1804.02767 http://arxiv.org/abs/1804.02767
Reingold, E.M.: Eye tracking research and technology: towards objective measurement of data quality. Vis. Cogn. 22(3), 635–652 (2014). https://doi.org/10.1080/13506285.2013.876481
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017). https://doi.org/10.1109/TPAMI.2016.2577031
Roy, D., Ghitza, Y., Bartelma, J., Kehoe, C.: Visual memory augmentation: using eye gaze as an attention filter. In: Eighth International Symposium on Wearable Computers, vol. 1, pp. 128–131. https://doi.org/10.1109/ISWC.2004.47
Salminen, J., Jansen, B.J., An, J., Jung, S.-G., Nielsen, L., Kwak, H.: Fixation and confusion: investigating eye-tracking participants’ exposure to information in personas. In: Proceedings of the 2018 Conference on Human Information Interaction & Retrieval (New Brunswick, NJ, USA) (CHIIR 2018), pp. 110–119. Association for Computing Machinery, New York. https://doi.org/10.1145/3176349.3176391
Schöning, J., Faion, P., Heidemann, G., Krumnack, U.: Providing Video Annotations in Multimedia Containers for Visualization and Research (2017). https://doi.org/10.1109/WACV.2017.78
Schwab, D., Fejza, A., Vial, L., Robert, Y.: The gazeplay project: open and free eye-trackers games and a community for people with multiple disabilities. In: Miesenberger, K., Kouroupetroglou, G. (eds.) Computers Helping People with Special Needs, pp. 254–261. Springer, Cham. https://doi.org/10.1007/978-3-319-94277-3_41
SensoMotoric. [n.d.]. http://www.smivision.com/. Accessed 3 Mar 2020
Sesma, L., Villanueva, A., Cabeza, R.: Evaluation of pupil center-eye corner vector for gaze estimation using a web cam. In: Proceedings of the Symposium on Eye Tracking Research and Applications (Santa Barbara, California) (ETRA 2012), pp. 217–220. Association for Computing Machinery, New York (2012). https://doi.org/10.1145/2168556.2168598
Sewell, W., Komogortsev, O.: Real-time eye gaze tracking with an unmodified commodity webcam employing a neural network. In: CHI 2010 Extended Abstracts on Human Factors in Computing Systems (Atlanta, Georgia, USA) (CHI EA 2010), pp. 3739–3744. Association for Computing Machinery, New York. https://doi.org/10.1145/1753846.1754048
Shih, S.-W., Liu, J.: A novel approach to 3-D gaze tracking using stereo cameras. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 34(1), 234–245 (2004). https://doi.org/10.1109/TSMCB.2003.811128
Shih, S.-W., Wu, Y.-T., Liu, J.: A calibration-free gaze tracking technique. In: Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, vol. 4, pp. 201–204 (2000)
Shrivastava, A., Pfister, T., Tuzel, O., Susskind, J., Wang, W., Webb, R.: Learning from simulated and unsupervised images through adversarial training. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2242–2251 (2017). https://doi.org/10.1109/CVPR.2017.241
Sigut, J., Sidha, S.: Iris center corneal reflection method for gaze tracking using visible light. IEEE Trans. Biomed. Eng. 58(2), 411–419 (2011). https://doi.org/10.1109/TBME.2010.2087330
Sims, S.D., Putnam, V., Conati, C.: Predicting Confusion from Eye-Tracking Data with Recurrent Neural Networks. CoRR abs/1906.11211 (2019). arXiv:1906.11211 http://arxiv.org/abs/1906.11211
Sireesha, M.V., Vijaya, P.A., Chellamma, K.: A survey on gaze estimation techniques. In: Chakravarthi, V.S., Shirur, Y.J.M., Prasad, R. (eds.) Proceedings of International Conference on VLSI, Communication, Advanced Devices, Signals & Systems and Networking (VCASAN-2013), pp. 353–361. Springer, Heidelberg. https://doi.org/10.1007/978-81-322-1524-0_43
Smith, B.A., Yin, Q., Feiner, S.K., Nayar, S.K.: Gaze locking: passive eye contact detection for human-object interaction. In: Proceedings of the 26th Annual ACM Symposium on User Interface Software and Technology (St. Andrews, Scotland, United Kingdom) (UIST 2013), pp. 271–280. Association for Computing Machinery, New York. https://doi.org/10.1145/2501988.2501994
Smith, P., Shah, M., da Vitoria Lobo, N.: Determining driver visual attention with one camera. IEEE Trans. Intell. Transp. Syst. 4(4), 205–218 (2003). https://doi.org/10.1109/TITS.2003.821342
Steil, J., Hagestedt, I., Huang, M.X., Bulling, A.: Privacy-Aware Eye Tracking Using Differential Privacy. CoRR abs/1812.08000 (2018). arXiv:1812.08000 http://arxiv.org/abs/1812.08000
Strupczewski, A.: Commodity Camera Eye Gaze Tracking. Ph.D. Dissertation. Warsaw University of Technology (2016)
Sugano, Y., Matsushita, Y., Sato, Y.: Appearance-based gaze estimation using visual saliency. IEEE Trans. Pattern Anal. Mach. Intell. 35(2), 329–341 (2013)
Sugano, Y., Matsushita, Y., Sato, Y.: Learning-by-synthesis for appearance-based 3D gaze estimation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1821–1828 (2014). https://doi.org/10.1109/cvpr.2014.235
Sun, L., Liu, Z., Sun, M.-T.: Real time gaze estimation with a consumer depth camera. Inf. Sci. 320(2015), 346–360 (2015). https://doi.org/10.1016/j.ins.2015.02.004
Söylemez, Ö.F., Ergen, B.: Circular Hough transform based eye state detection in human face images. In: 2013 21st Signal Processing and Communications Applications Conference (SIU), pp. 1–4. https://doi.org/10.1109/SIU.2013.6531537
Tateno, K., Takemura, M., Ohta, Y.: Enhanced eyes for better gaze-awareness in collaborative mixed reality. In: Fourth IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR 2005), pp. 100–103. https://doi.org/10.1109/ISMAR.2005.29
Tobii. [n.d.]. https://www.tobii.com/. Accessed 3 Mar 2020
Pro Tobii. Sticky (2019). https://www.tobiipro.com/product-listing/sticky-by-tobii-pro/
tobiidynavox.com. [n.d.]. How to get a good calibration. https://www.tobiidynavox.com/supporttraining/eye-tracker-calibration/how-to-get-a-good-calibration/. Accessed 16 Sept 2019
Tomono, A., Iida, M., Kobayashi, Y.: A TV camera system which extracts feature points for non-contact eye movement detection. In: Optics, Illumination, and Image Sensing for Machine Vision IV, Donald J. Svetkoff (Ed.), vol. 1194, pp. 2–20. International Society for Optics and Photonics, SPIE (1990). https://doi.org/10.1117/12.969833
Tonsen, M., Steil, J., Sugano, Y., Bulling, A.: InvisibleEye: mobile eye tracking using multiple low-resolution cameras and learning-based gaze estimation. In: Proceedings ACM Interaction Mobile Wearable Ubiquitous Technology, Article 106, p. 21 (2017). https://doi.org/10.1145/3130971
TurkerGaze. [n.d.]. https://github.com/PrincetonVision/TurkerGaze
Valenti, R., Sebe, N., Gevers, T.: Combining head pose and eye location information for gaze estimation. IEEE Trans. Image Process. 21(2), 802–815. https://doi.org/10.1109/TIP.2011.2162740
Villanueva, A., Cabeza, R.: A novel gaze estimation system with one calibration point. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 38(4), 1123–1138 (2008)
Villanueva, A., Ponz, V., Sesma-Sanchez, L., Ariz, M., Porta, S., Cabeza, R.: Hybrid method based on topography for robust detection of iris center and eye corners. ACM Trans. Multimedia Comput. Commun. Appl. 9(4), 20 (2013). Article 25, https://doi.org/10.1145/2501643.2501647
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, vol. 1, p. I. https://doi.org/10.1109/CVPR.2001.990517
Sung, W., Venkateswarlu, R.: Eye gaze estimation from a single image of one eye. In: Proceedings Ninth IEEE International Conference on Computer Vision, vol. 1, pp. 136–143. https://doi.org/10.1109/ICCV.2003.1238328
Wang, X., Liu, K., Qian, X.: A survey on gaze estimation. In: 2015 10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), pp. 260–267. https://doi.org/10.1109/ISKE.2015.12
Wang, Y., Shen, T., Yuan, G., Bian, J., Fu, X.: Appearance-based gaze estimation using deep features and random forest regression. Knowl.-Based Syst. 110 (2016). https://doi.org/10.1016/j.knosys.2016.07.038
Wang, Y., Yuan, G., Mi, Z., Peng, J., Ding, X., Liang, Z., Fu, X.: Continuous driver’s gaze zone estimation using RGB-D camera. Sensors 19, 6 (2019). https://doi.org/10.3390/s19061287
Wang, Y., Zhao, T., Ding, X., Peng, J., Bian, J., Fu, X.: Learning a gaze estimator with neighbor selection from large-scale synthetic eye images. Knowl.-Based Syst. 139, 41–49 (2017). https://doi.org/10.1016/j.knosys.2017.10.010
Weaver, J., Mock, K., Hoanca, B.: Gaze-based password authentication through automatic clustering of gaze points. In: 2011 IEEE International Conference on Systems, Man, and Cybernetics, pp. 2749–2754. https://doi.org/10.1109/ICSMC.2011.6084072
Strauss, P.-M., Neumann, H., Weidenbacher, U., Layher, G.: A comprehensive head pose and gaze database. In: IET Conference Proceedings, pp. 455–458 (2007). https://doi.org/10.1049/cp:20070407
Williams, O., Blake, A., Cipolla, R.: Sparse and semisupervised visual mapping with the S 3 GP. In: CVPR, p. 230 (2006)
Wood, E., Baltruaitis, T., Zhang, X., Sugano, Y., Robinson, P., Bulling, A.: Rendering of eyes for eye-shape registration and gaze estimation. In: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV) (ICCV 2015), pp. 3756–3764. IEEE Computer Society, USA. https://doi.org/10.1109/ICCV.2015.428
Wood, E., Baltrušaitis, T., Morency, L.-P., Robinson, P., Bulling, A.: Learning an appearance based gaze estimator from one million synthesised images. In: Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications (Charleston, South Carolina) (ETRA 2016), pp. 131–138. Association for Computing Machinery, New York (2016). https://doi.org/10.1145/2857491.2857492
Wood, E., Bulling, A.: EyeTab: model-based gaze estimation on unmodified tablet computers. In: Eye Tracking Research and Applications Symposium (ETRA), pp. 207–210 (2014). https://doi.org/10.1145/2578153.2578185
Wu, H., Kitagawa, Y., Wada, T., Kato, T., Chen, Q.: Tracking iris contour with a 3D eye-model for gaze estimation. In: Kweon, S., Zha, H. (eds.) Computer Vision – ACCV 2007, pp. 688–697. Springer, Berlin. https://doi.org/10.1007/978-3-540-76386-4_65
Xiong, X., Cai, Q., Liu, Z., Zhang, Z.: Eye gaze tracking using an RGBD camera: a comparison with an RGB solution. In: The 4th International Workshop on Pervasive Eye Tracking and Mobile Eye-Based Interaction (PETMEI 2014) (the 4th International Workshop on Pervasive Eye Tracking and Mobile Eye-Based Interaction (petmei 2014)). ACM – Association for Computing Machinery. https://www.microsoft.com/en-us/research/publication/eye-gaze-tracking-using-an-rgbd-camera-acomparison-with-an-rgb-solution/
Xlabs. [n.d.]. https://xlabsgaze.com/. Accessed 4 Feb 2020
Young, L.R., Sheena, D.: Survey of eye movement recording methods. Behav. Res. Methods Instrum. 7(5), 397–429 (1975). https://doi.org/10.3758/BF03201553
Yu, Y., Liu, G., Odobez, J.: Improving few-shot user-specific gaze adaptation via gaze redirection synthesis. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11929–11938 (2019). https://doi.org/10.1109/CVPR.2019.01221
Yu, Y., Liu, G., Odobez, J.-M.: Deep multitask gaze estimation with a constrained landmark-gaze model. In: Leal-Taixé, L., Roth, S. (eds.) Computer Vision – ECCV 2018 Workshops, pp. 456–474. Springer, Cham. https://doi.org/10.1007/978-3-030-11012-3_35
Zhang, X., Huang, M.X., Sugano, Y., Bulling, A.: Training person-specific gaze estimators from user interactions with multiple devices. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (Montreal QC, Canada) (CHI 2018), p. 12. Association for Computing Machinery, New York, Article Paper 624. https://doi.org/10.1145/3173574.3174198
Zhang, X., Sugano, Y., Bulling, A.: Evaluation of appearance-based methods and implications for gaze-based applications. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI 2019), p. 13. Association for Computing Machinery, New York, Article Paper 416. https://doi.org/10.1145/3290605.3300646
Zhang, X., Sugano, Y., Fritz, M., Bulling, A.: Appearance-based gaze estimation in the wild. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4511–4520. https://doi.org/10.1109/CVPR.2015.7299081
Zhang, X., Sugano, Y., Fritz, M., Bulling, A.: It’s written all over your face: full-face appearance-based gaze estimation. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 2299–2308 (2017). https://doi.org/10.1109/CVPRW.2017.284
Zhang, X., Sugano, Y., Fritz, M., Bulling, A.: MPIIGaze: real-world dataset and deep appearance-based gaze estimation. IEEE Trans. Pattern Anal. Mach. Intell. 41(1), 162–175 (2019). https://doi.org/10.1109/TPAMI.2017.2778103
Zhang, Y., Chong, M.K., Müller, J., Bulling, A., Gellersen, H.: Eye tracking for public displays in the wild. Pers. Ubiquitous Comput. 19(5), 967–981 (2015). https://doi.org/10.1007/s00779-015-0866-8
Yao, R., Cai-J Zhang, C.: Efficient eye typing with 9-direction gaze estimation. Multimed Tools Appl. 77(2018), 19679–19696 (2018). https://doi.org/10.1007/s11042-017-5426-y
Zhao, T., Yan, Y., Shehu, I.S., Fu, X.: Image purification networks: real-time style transfer with semantics through feed-forward synthesis. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–7 (2018). https://doi.org/10.1109/IJCNN.2018.8489365
Zhao, T., Yan, Y., Shehu, I.S., Xianping, F., Wang, H.: Purifying naturalistic images through a real-time style transfer semantics network. Eng. Appl. Artif. Intell. 81(2019), 428–436 (2019). https://doi.org/10.1016/j.engappai.2019.02.011
Zhao, T., Yan, Y., Shehu, I.S., Wei, H., Fu, X.: Image purification through controllable neural style transfer. In: 2018 International Conference on Information and Communication Technology Convergence (ICTC), pp. 466–471 (2018). https://doi.org/10.1109/ICTC.2018.8539637
Zhu, Z., Ji, Q.: Eye gaze tracking under natural head movements. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 918–923 (2005). https://doi.org/10.1109/CVPR.2005.148
Zhu, Z., Ji, Q., Bennett, K.P.: Nonlinear eye gaze mapping function estimation via support vector regression. In: 18th International Conference on Pattern Recognition (ICPR 2006), vol. 1, pp. 1132–1135 (2006). https://doi.org/10.1109/ICPR.2006.864
Zhu, J., Yang, J.: Subpixel eye gaze tracking. In: Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition (FGR 2002), p. 131. IEEE Computer Society, USA (2002). https://doi.org/10.5555/874061.875453
Ji, Q., Zhu, Z.: Eye and gaze tracking for interactive graphic display. Mach. Vis. Appl. 15(2004), 139–148 (2004). https://doi.org/10.1007/s00138-004-0139-4
Zhu, Z., Ji, Q.: Novel eye gaze tracking techniques under natural head movement. IEEE Trans. Biomed. Eng. 54(12), 2246–2260 (2007). https://doi.org/10.1109/TBME.2007.895750
Wu, M., et al.: Gaze-based intention anticipation over driving manoeuvres in semi-autonomous vehicles. In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, pp. 6210–6216 (2019). https://doi.org/10.1109/IROS40897.2019.8967779
Subramanian, M., Songur, N., Adjei, D., Orlov, P., Faisal, A.A.: A.Eye Drive: Gaze-based semi-autonomous wheelchair interface. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, pp. 5967–5970 (2019). https://doi.org/10.1109/EMBC.2019.8856608
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China Grant 61802043 and Grant 61370142, by the Liaoning Revitalization Talents Program Grant XLYC1908007, by the Foundation of Liaoning Key Research and Development Program Grant 201801728, by the Fundamental Research Funds for the Central Universities Grant 3132016352 and Grant 3132020215, by the Dalian Science and Technology Innovation Fund 2018J12GX037 and 2019J11CY001.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Shehu, I.S., Wang, Y., Athuman, A.M., Fu, X. (2021). Paradigm Shift in Remote Eye Gaze Tracking Research: Highlights on Past and Recent Progress. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Proceedings of the Future Technologies Conference (FTC) 2020, Volume 1. FTC 2020. Advances in Intelligent Systems and Computing, vol 1288. Springer, Cham. https://doi.org/10.1007/978-3-030-63128-4_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-63128-4_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63127-7
Online ISBN: 978-3-030-63128-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)