Chapter published in:
Eye-tracking in Interaction: Studies on the role of eye gaze in dialogue
Edited by Geert Brône and Bert Oben
[Advances in Interaction Studies 10] 2018
► pp. 139168
Al Moubayed, S., Edlund, J., & Beskow, J.
(2012) Taming Mona Lisa: communicating gaze faithfully in 2D and 3D facial projections. ACM Transactions on Interactive Intelligent Systems, 1(2), article 11 (25pages).CrossrefGoogle Scholar
Al Moubayed, S., Skantze, G., & Beskow, J.
(2012) Lip-reading: Furhat audiovisual intelligibility of a back-projected animated face. Intelligent Virtual Agents – Lecture Notes in Computer Science, 7502, 196–203.CrossrefGoogle Scholar
Albrecht, I., Haber, J., & Seidel, H. -P.
(2002) Automatic Generation of Non-Verbal Facial Expressions from Speech. In J. Vince & R. Earnshaw (Eds.), Advances in Modelling, Animation and Rendering (pp. 283–293). Springer London. Retrieved from CrossrefGoogle Scholar
Allopenna, P. D., Magnuson, J. S., & Tanenhaus, M. K.
(1998) Tracking the time course of spoken word recognition using eye movements: Evidence for continuous mapping models. Journal of Memory and Language, 38(4), 419–439.CrossrefGoogle Scholar
Alnajar, F., Gevers, T., Valenti, R., & Ghebreab, S.
(2013) Calibration-free gaze estimation using human gaze patterns (pp.137–144). Presented at the Computer Vision (ICCV), 2013 IEEE International Conference on, Sydney, Australia: IEEE.Google Scholar
Bailly, G., Elisei, F., Raidt, S., Casari, A., & Picot, A.
(2006) Embodied conversational agents : computing and rendering realistic gaze patterns. In Pacific Rim Conference on Multimedia Processing (Vol. LNCS 4261, pp.9–18). Hangzhou – China.Google Scholar
Bailly, G., Elisei, F., & Sauze, M.
(2015) Beaming the gaze of a humanoid robot. In Human-Robot Interaction (HRI) (pp.47–48). Portland, OR.Google Scholar
Bailly, G., Raidt, S., & Elisei, F.
(2010) Gaze, conversational agents and face-to-face communication. Speech Communication – Special Issue on Speech and Face-to-Face Communication, 52(3), 598–612.Google Scholar
Bajcsy, R.
(1988) Active Perception. IEEE, Special Issue on Computer Vision76(8), 996–1005.Google Scholar
Barisic, I., Timmermans, B., Pfeiffer, U., Bente, G., Vogeley, K., & Schilbach, L.
(2013) Using dual eyetracking to investigate real-time social interactions. Proceedings from SIGCHI Conference on Human Factors in Computing Systems.Google Scholar
Baron-Cohen, S., Jollife, T., Mortimore, C., & Robertson, M.
(1997) Another advanced test of theory of mind: evidence from very high functioning adults with autism or Asperger syndrome. Journal of Child Psychology and Psychiatry, 38(7), 813–822.CrossrefGoogle Scholar
Bengio, Y., & Frasconi, P.
(1996) Input-output HMMs for sequence processing. IEEE Transactions on Neural Networks, 7(5), 1231–1249. CrossrefGoogle Scholar
Benoît, C., Grice, M., & Hazan, V.
(1996) The SUS test: A method for the assessment of text-to-speech synthesis intelligibility using Semantically Unpredictable Sentences. Speech Communication, 18, 381–392.CrossrefGoogle Scholar
Bindemann, M., Burton, A. M., Hooge, I. C., Jenkins, R., &de Haan, E. F.
(2005) Faces retain attention. Psychonomic Bulletin & Review, 12(6), 1048–1053. CrossrefGoogle Scholar
Boker, S. M., Cohn, J. F., Theobald, B. -J., Matthews, I., Brick, T. R., & Spies, J. R.
(2009) Effects of damping head movement and facial expression in dyadic conversation using real-time facial expression tracking and synthesized avatars. Philosophical Transactions of the Royal Society – Biological Sciences, 364(1535), 3485–3495.CrossrefGoogle Scholar
Bolt, R.A.
1980Put-that-there: Voice and gesture at the graphics interface. ACM SIGGRAPH Computer Graphics 14, 262-270.CrossrefGoogle Scholar
Borji, A., Sihite, D. N., & Itti, L.
(2013) Quantitative Analysis of Human-Model Agreement in Visual Saliency Modeling: A Comparative Study. Image Processing, IEEE Transactions on, 22(1), 55–69. CrossrefGoogle Scholar
Brône, G., & Oben, B.
(2015) InSight Interaction: a multimodal and multifocal dialogue corpus. Language Resources and Evaluation, 49(1), 195–214. CrossrefGoogle Scholar
Buchan, J. N., Paré, M., & Munhall, K. G.
(2007) Spatial statistics of gaze fixations during dynamic face processing. Social Neuroscience, 2(1), 1–13.CrossrefGoogle Scholar
Carletta, J., Hill, R. L., Nicol, C., Taylor, T., de Ruiter, J. P., & Bard, E. G.
(2010) Eyetracking for two-person tasks with manipulation of a virtual world. Behavior Research Methods, 42(1), 254–265. CrossrefGoogle Scholar
Clark, H. H.
(2003) Pointing and placing. In S. Kita (Ed.), Pointing: Where Language, Culture, and Cognition Meet (pp.243–268). New York: Lawrence Erlbaum Associates Publishers.Google Scholar
Cooper, G. F., & Herskovits, E.
(1992) A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9(4), 309–347. CrossrefGoogle Scholar
Coutrot, A., & Guyader, N.
(2014) How saliency, faces, and sound influence gaze in dynamic social scenes. Journal of Vision, 14(8), 5.CrossrefGoogle Scholar
Coutrot, A., Guyader, N., Ionescu, G., & Caplier, A.
(2012) Influence of soundtrack on eye movements during video exploration. Journal of Eye Movement Research, 5(4), 2.Google Scholar
Cuijpers, R. H., & van der Pol, D.
(2013) Region of eye contact of humanoid Nao robot is similar to that of a human. In G. Herrmann, M. J. Pearson, A. Lenz, P. Bremner, A. Spiers, & U. Leonards (Eds.), Social Robotics (Vol. 8239, pp.280–289). Springer International Publishing. Retrieved from CrossrefGoogle Scholar
Cummins, F.
(2012) Gaze and blinking in dyadic conversation: A study in coordinated behaviour among individuals. Language and Cognitive Processes, 27(10), 1525–1549.CrossrefGoogle Scholar
Dale, R., Fusaroli, R., Duran, N., & Richardson, D. C.
(2013) The self-organization of human interaction. Psychology of Learning and Motivation, 59, 43–95.CrossrefGoogle Scholar
de Kok, I.
(2013) Listening heads (PhD Thesis). University of Twente, Enschede, The Netherlands.CrossrefGoogle Scholar
Delaunay, F., Greeff, J., & Belpaeme, T.
(2010) A study of a retro-projected robotic face and its effectiveness for gaze reading by humans. In ACM/IEEE International Conference on Human-Robot Interaction (HRI) (pp.39–44). Osaka, Japan.Google Scholar
Donat, R., Bouillaut, L., Aknin, P., & Leray, P.
(2008) Reliability analysis using graphical duration models (pp.795–800). Presented at the Availability, Reliability and Security, 2008. ARES 08. Third International Conference on, IEEE.Google Scholar
Duffner, S., & Garcia, C.
(2015) Visual Focus of Attention estimation with unsupervised incremental learning. IEEE Transactions on Circuits and Systems for Video Technology, to appear.Google Scholar
Elisei, F., Bailly, G., & Casari, A.
(2007) Towards eyegaze-aware analysis and synthesis of audiovisual speech. In Auditory-visual Speech Processing (pp.120–125). Hilvarenbeek, The Netherlands.Google Scholar
Ferreira, J. F., Lobo, J., Bessiere, P., Castelo-Branco, M., & Dias, J.
(2013) A Bayesian framework for active artificial perception. IEEE Transactions on Cybernetics, 43(2), 699–711. CrossrefGoogle Scholar
Foerster, F., Bailly, G., & Elisei, F.
(2015) Impact of iris size and eyelids coupling on the estimation of the gaze direction of a robotic talking head by human viewers. In Humanoids. Seoul, Korea. 148–153.Google Scholar
Ford, C.E.
2004Contingency and units in interaction. Discourse Studies 6, 27-52.CrossrefGoogle Scholar
Foulsham, T., Walker, E., & Kingstone, A.
(2011) The where, what and when of gaze allocation in the lab and the natural environment. Vision Research, 51(17), 1920–1931. CrossrefGoogle Scholar
Fuller, J. H.
(1992) Head movement propensity. Experimental Brain Research, 92(1), 152–164.CrossrefGoogle Scholar
Funes Mora, K. A., & Odobez, J. -M.
(2014) Geometric generative gaze estimation (G3E) for remote RGB-D cameras (pp.1773–1780). Presented at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH: IEEE.Google Scholar
Fusaroli, R., & Tylén, K.
(2016) Investigating conversational dynamics: Interactive alignment, Interpersonal synergy, and collective task performance. Cognitive Science, 40(1), 145–171.CrossrefGoogle Scholar
Garau, M., Slater, M., Bee, S., & Sasse, M. A.
(2001) The impact of eye gaze on communication using humanoid avatars. In SIGCHI conference on Human factors in computing systems (pp.309–316). Seattle, WA.Google Scholar
Goferman, S., Zelnik-Manor, L., & Tal, A.
(2012) Context-aware saliency detection. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 34(10), 1915–1926.CrossrefGoogle Scholar
Gregory, R.
(1997) Eye and Brain: The Psychology of Seeing. Princeton, NJ: Princeton University Press.Google Scholar
Gu, E., & Badler, N. I.
(2006) Visual attention and eye gaze during multiparty conversations with distractions (pp.193–204). Presented at the Intelligent Virtual Agents, Springer.Google Scholar
Hanes, D. A., & McCollum, G.
(2006) Variables contributing to the coordination of rapid eye/head gaze shifts. Biological Cybernetics, 94, 300–324.CrossrefGoogle Scholar
Henderson, J. M., Malcolm, G. L., & Schandl, C.
(2009) Searching in the dark: Cognitive relevance drives attention in real-world scenes. Psychonomic Bulletin & Review, 16(5), 850–856. CrossrefGoogle Scholar
Hietanen, J. K.
(1999) Does your gaze direction and head orientation shift my visual attention? Neuroreport, 10(16), 3443–3447.CrossrefGoogle Scholar
Hochreiter, S., & Schmidhuber, J.
(1997) Long short-term memory. Neural Computation, 9(8), 1735–1780.CrossrefGoogle Scholar
Huang, C. -M., & Mutlu, B.
(2014) Learning-based Modeling of Multimodal Behaviors for Humanlike Robots. In Proceedings of the 2014 ACM/IEEE International Conference on Human-robot Interaction (pp. 57–64). New York, NY, USA: ACM. CrossrefGoogle Scholar
Ishii, R., Otsuka, K., Kumano, S., & Yamato, J.
(2014) Analysis and modeling of next speaking start timing based on gaze behavior in multi-party meetings. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp.694–698). Florence, Italy.Google Scholar
Itti, L., Dhavale, N., & Pighin, F.
(2003) Realistic avatar eye and head animation using a neurobiological model of visual attention. In SPIE 48th Annual International Symposium on Optical Science and Technology (Vol. 5200, pp.64–78). Bellingham, WA.Google Scholar
(2006) Photorealistic attention-based gaze animation. In IEEE International Conference on Multimedia and Expo (pp. 521–524). Toronto, Canada.Google Scholar
Jensen, F., Lauritzen, S., & Olesen, K.
(1990) Bayesian updating in recursive graphical models by local computations. Computational Statistics Quaterly, 4(1), 269–282.Google Scholar
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., & Fei-Fei, L.
(2014) Large-scale video classification with convolutional neural networks (pp.1725–1732). Presented at the Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, IEEE.Google Scholar
Kobayashi, H., & Kohshima, S.
(2001) Unique morphology of the human eye and its adaptive meaning: comparative studies on external morphology of the primate eye. Journal of Human Evolution, 40(5), 419–435.CrossrefGoogle Scholar
Koller, D., & Friedman, N.
(2009) Probabilistic Graphical Models: Principles and Techniques – Adaptive Computation and Machine Learning. Boston, MA: MIT Press.Google Scholar
Krizhevsky, A., Sutskever, I., & Hinton, G. E.
(2012) ImageNet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing (NIPS). Lake Tahoe, NV.Google Scholar
Laidlaw, K. E. W., Foulsham, T., Kuhn, G., & Kingstone, A.
(2011) Social attention to a live person is critically different than looking at a videotaped person. PNAS, 108, 5548–5553. CrossrefGoogle Scholar
Lakin, J., Jefferis, V., Cheng, C., & Chartrand, T.
(2003) The chameleon effect as social glue: evidence for the evolutionary significance of nonconscious mimicry. Nonverbal Behavior, 27(3), 145–162.CrossrefGoogle Scholar
Langton, S. R. H.
(2000) The mutual influence of gaze and head orientation in the analysis of social attention direction. Quarterly Journal of Experimental Psychology, 53A(3), 825–845.CrossrefGoogle Scholar
Langton, S. R., Honeyman, H., & Tessler, E.
(2004) The influence of head contour and nose angle on the perception of eye-gaze direction. Perception & Psychophysics, 66(5), 752–771.CrossrefGoogle Scholar
Lansing, C. R., & McConkie, G. W.
(1999) Attention to facial regions in segmental and prosodic visual speech perception tasks. Journal of Speech, Language, and Hearing Research, 42(3), 526–539.CrossrefGoogle Scholar
Lee, S. P., Badler, J. B., & Badler, N.
(2002) Eyes alive. ACM Transaction on Graphics, 21(3), 637–644.CrossrefGoogle Scholar
Levenshtein, V.
(1966) Binary Codes Capable of Correcting Deletions, Insertions and Reversals. Soviet Physics Doklady , 10(8), 707–710.Google Scholar
Li, J., Tian, Y., & Huang, T.
(2014) Visual saliency with statistical priors. International Journal of Computer Vision, 107(3), 239–253.CrossrefGoogle Scholar
Liang, S., Fuhrman, S., Somogyi, R.
, & others (1998) Reveal, a general reverse engineering algorithm for inference of genetic network architectures. In Pacific symposium on biocomputing (Vol. 3, pp.18–29).Google Scholar
Marschner, L., Pannasch, S., Schulz, J., & Graupner, S. -T.
(2015) Social communication with virtual agents: The effects of body and gaze direction on attention and emotional responding in human observers. International Journal of Psychophysiology, 97(2), 85–92. CrossrefGoogle Scholar
McNeill, D.
(1992) Hand and Mind. What Gestures Reveal about Thought. Chicago: Chicago University Press.Google Scholar
Mihoub, A., Bailly, G., & Wolf, C.
(2014) Modelling perception-action loops: comparing sequential models with frame-based classifiers. In Human-Agent Interaction (HAI) (pp.309–314). Tsukuba, Japan.Google Scholar
(2015) Learning multimodal behavioral models for face-to-face social interaction. Journal on Multimodal User Interfaces, 9(3), 195–210. CrossrefGoogle Scholar
Mihoub, A., Bailly, G., Wolf, C., & Elisei, F.
(2016) Graphical models for social behavior modeling in face-to face interaction. Pattern Recognition Letters, 74, 82–89. CrossrefGoogle Scholar
Murphy, K.
(2002) Dynamic bayesian networks: representation, inference and learning (PhD Thesis). UC Berkeley, Computer Science Division, Berkeley, CA.Google Scholar
Murphy, K. P.
(2001) The Bayes Net Toolbox for MATLAB. Computing Science and Statistics, 33, 2001.Google Scholar
Mutlu, B., Kanda, T., Forlizzi, J., Hodgins, J., & Ishiguro, H.
(2012) Conversational gaze mechanisms for humanlike robots. ACM Transactions on Interactive Intelligent Systems (TiiS), 1(2), 12.Google Scholar
Neverova, N., Wolf, C., Taylor, G. W., & Nebout, F.
(2016) ModDrop: adaptive multi-modal gesture recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 38(8), 1692–1706.CrossrefGoogle Scholar
Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., & Ng, A. Y.
(2011) Multimodal deep learning (pp.689–696). Presented at the International conference on machine learning (ICML), Bellevue, WA.Google Scholar
Nguyen, D.-A., Bailly, G., & Elisei, F.
(2016) Conducting neuropsychological tests with a humanoid robot: design and evaluation. In IEEE International Conference on Cognitive Infocommunications – CogInfoCom. Wroclaw, Poland. 337–342.Google Scholar
Onuki, T., Ishinoda, T., Kobayashi, Y., & Kuno, Y.
(2013) Designing robot eyes for gaze communication. In IEEE Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV) (pp.97–102). Fukuoka, Japan.CrossrefGoogle Scholar
Otsuka, K.
(2011) Multimodal Conversation Scene Analysis for Understanding People’s Communicative Behaviors in Face-to-Face Meetings. In International Conference on Human-Computer Interaction (HCI) (Vol. 12, pp.171–179). Orlando FL.Google Scholar
Otsuka, K., Takemae, Y., & Yamato, J.
(2005) A probabilistic inference of multiparty-conversation structure based on Markov-switching models of gaze patterns, head directions, and utterances. In International Conference on Multimodal Interfaces (ICMI) (pp.191–198). Seattle, WA.Google Scholar
Oyekoya, O., Steed, A., & Steptoe, W.
(2010) Eyelid kinematics for virtual characters. Computer Animation and Virtual Worlds, 21(3–4), 161–171.Google Scholar
Pelachaud, C.&Bilvi, M.
(2003) Modelling gaze behavior for conversational agents. In International Working Conference on Intelligent Virtual Agents (Vol. LNAI 2792). Kloster Irsee, Germany.CrossrefGoogle Scholar
Pentland, A. S.
(2004) Social dynamics: Signals and behavior. Presented at the International Conference on Developmental Learning, La Jolla, CA.Google Scholar
(2007) Social Signal Processing. IEEE Signal Processing Magazine, 24(4), 108–111.CrossrefGoogle Scholar
Picot, A., Bailly, G., Elisei, F., & Raidt, S.
(2007) Scrutinizing natural scenes: controlling the gaze of an embodied conversational agent. In International Conference on Intelligent Virtual Agents (IVA) (pp.272–282). Paris, France.CrossrefGoogle Scholar
Raidt, S., Bailly, G., & Elisei, F.
(2007) Mutual gaze during face-to-face interaction. In Auditory-visual Speech Processing. Hilvarenbeek, The Netherlands. paper P23, 6 pagesGoogle Scholar
Richardson, D. C., Dale, R., & Kirkham, N. Z.
(2007) The art of conversation is coordination common ground and the coupling of eye movements during dialogue. Psychological Science, 18(5), 407–413.CrossrefGoogle Scholar
Richardson, D. C., Dale, R., & Shockley, K.
(2008) Synchrony and swing in conversation: coordination, temporal dynamics, and communication. In I. Wachsmuth, M. Lenzen, & G. Knoblich (Eds.), Embodied Communication (pp. 75–93). Oxford, UK: Oxford University Press.Google Scholar
Risko, E. F., Laidlaw, K. E. W., Freeth, M., Foulsham, T., & Kingstone, A.
(2012) Social attention with real versus reel stimuli: toward an empirical approach to concerns about ecological validity. Frontiers in Human Neuroscience, 6, 143. CrossrefGoogle Scholar
Risko, E. F., Richardson, D. C., & Kingstone, A.
(2016) Breaking the Fourth Wall of Cognitive Science Real-World Social Attention and the Dual Function of Gaze. Current Directions in Psychological Science, 25(1), 70–74.CrossrefGoogle Scholar
Ruhland, K., Andrist, S., Badler, J., Peters, C., Badler, N., Gleicher, M.&R. Mcdonnell
(2014) Look me in the eyes: A survey of eye and gaze animation for virtual agents and artificial systems (pp.69–91). Presented at the Eurographics State-of-the-Art Report.Google Scholar
Sak, H., Vinyals, O., Heigold, G., Senior, A., McDermott, E., Monga, R., & Mao, M.
(2014) Sequence discriminative distributed training of long short-term memory recurrent neural networks. Entropy, 15(16), 17–18.Google Scholar
Schauerte, B., & Stiefelhagen, R.
(2014) “Look at this!” learning to guide visual saliency in human-robot interaction (pp.995–1002). Presented at the Intelligent Robots and Systems (IROS 2014), 2014 IEEE/RSJ International Conference on, IEEE.Google Scholar
Schmidt, R., Morr, S., Fitzpatrick, P., & Richardson, M. J.
(2012) Measuring the dynamics of interactional synchrony. Journal of Nonverbal Behavior, 36(4), 263–279.CrossrefGoogle Scholar
Senju, A., & Hasegawa, T.
(2005) Direct gaze captures visuospatial attention. Vision Cognition, 12, 127– 144.CrossrefGoogle Scholar
Sheikhi, S., Odobez, J.-M.
2014Combining dynamic head pose-gaze mapping with the robot conversational state for attention recognition in human-robot interactions. Pattern Recognition Letters.Google Scholar
Sugano, Y., Matsushita, Y., & Sato, Y.
(2013) Appearance-based gaze estimation using visual saliency. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 35(2), 329–341.CrossrefGoogle Scholar
Sugano, Y., Matsushita, Y., Sato, Y.
2014Learning-by-synthesis for appearance-based 3d gaze estimation. Presented at the Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, IEEE, pp. 1821-1828.Google Scholar
Sun, Y.
(2003) Hierarchical object-based visual attention for machine vision (Thesis). Institute of Perception, Action and Behaviour, University of Edinburgh, Edinburgh, UK.Google Scholar
Teufel, C., Alexis, D. M., Clayton, N. S., & Davis, G.
(2010) Mental-state attribution drives rapid, reflexive gaze following. Attention, Perception, & Psychophysics, 72(3), 695–705.CrossrefGoogle Scholar
Tomasello, M.
(2008) Origins of Human Communication. Boston, MA: MIT Press.Google Scholar
(2009) Why We Cooperate. Cambridge, MA: MIT Press.Google Scholar
Tomasello, M., Hare, B., Lehmann, H., & Call, J.
(2007) Reliance on head versus eyes in the gaze following of great apes and human infants: the cooperative eye hypothesis. Journal of Human Evolution, 52, 314–320.CrossrefGoogle Scholar
Trabelsi, G., Leray, P., Ben Ayed, M., & Alimi, A. M.
(2013) Benchmarking dynamic Bayesian network structure learning algorithms (pp.1–6). Presented at the Modeling, Simulation and Applied Optimization (ICMSAO), 2013 5th International Conference on, IEEE.Google Scholar
Trutoiu, L. C., Carter, E. J., Matthews, I., & Hodgins, J. K.
(2011) Modeling and animating eye blinks. ACM Transactions on Applied Perception (TAP), 8(3), 1–17. CrossrefGoogle Scholar
Valenti, R., & Gevers, T.
(2012) Accurate eye center location through invariant isocentric patterns. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 34(9), 1785–1798.CrossrefGoogle Scholar
Van der Burg, E., Olivers, C. N., Bronkhorst, A. W., & Theeuwes, J.
(2009) Poke and pop: Tactile–visual synchrony increases visual saliency. Neuroscience Letters, 450(1), 60–64.CrossrefGoogle Scholar
Vatikiotis-Bateson, E., Eigsti, I. -M., Yano, S., & Munhall, K. G.
(1998) Eye movement of perceivers during audiovisual speech perception. Perception & Psychophysics, 60, 926–940.CrossrefGoogle Scholar
Vertegaal, R., Slagter, R., van der Veer, G., & Nijholt, A.
(2001) Eye gaze patterns in conversations: There is more to conversational agents than meets the eyes. In Conference on Human Factors in Computing Systems (pp.301–308). Seattle, WA: ACM Press New York, NY, USA.Google Scholar
Vinayagamoorthy, V., Garau, M., Steed, A., & Slater, M.
(2004) An eye gaze model for dyadic interaction in an immersive virtual environment: Practice and experience. The Computer Graphics Forum, 23(1), 1–11.CrossrefGoogle Scholar
Võ, M. L. -H., Smith, T. J., Mital, P. K., & Henderson, J. M.
(2012) Do the eyes really have it? Dynamic allocation of attention when viewing moving faces. Journal of Vision, 13(3), 1–14.CrossrefGoogle Scholar
Yarbus, A. L.
(1967) Eye movements during perception of complex objects. In L. A. Riggs (Ed.), Eye Movements and Vision (Vol. VII, pp.171–196). New York: Plenum Press.CrossrefGoogle Scholar