Abstract
With the growing amount of mobile application usage, assuring a high quality of experience became more and more important. Besides traditional subjective methods to test and prototype new developments, eye tracking is a prominent tool to assess quality and UX of a software product. Although portable eye trackers exist, the technology is still mostly associated with expensive laboratory equipment. To change that and to run quick and cheap eye-tracking studies in the field, attempts have been made to turn everyday hardware like smartphone cameras and webcams into eye trackers. This study explores the possibility of using a standard library of iOS to tackle the vast technical complexity usually coming with such approaches. The accuracy of an eye-tracking system purely based on the ARKit APIs of iOS is evaluated in two user studies (N = 9 & N = 8). The results indicate that an ARKit based gaze tracker provides comparable performance in terms of accuracy (\(3.18^\circ \), or 1.44 cm on screen), while at the same time, it uses far fewer hardware resources and provides a higher sample-rate than any other smartphone eye tracker. Especially the easy to use API is the main advantage over the technical complex systems which rely on their own image analysis for gaze estimation. Privacy implications are discussed.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abbaszadegan, M., Yaghoubi, S., MacKenzie, I.S.: TrackMaze: a comparison of head-tracking, eye-tracking, and tilt as input methods for mobile games. In: Kurosu, M. (ed.) HCI 2018. LNCS, vol. 10903, pp. 393–405. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91250-9_31
Apple: Arkit 2 arfaceanchor - information about the pose, topology, and expression of a face detected in a face-tracking AR session (2018). https://developer.apple.com/documentation/arkit/arfaceanchor
Bates, R., Istance, H.O.: Why are eye mice unpopular? a detailed comparison of head and eye controlled assistive technology pointing devices. Univers. Inf. Soc. 2(3), 280–290 (2003)
Brousseau, B., Rose, J., Eizenman, M.: Hybrid eye-tracking on a smartphone with cnn feature extraction and an infrared 3D model. Sensors 20(2), 543 (2020)
Carl, J., Gellman, R.: Human smooth pursuit: stimulus-dependent responses. J. Neurophysiol. 57(5), 1446–1463 (1987)
Cicek, M., Xie, J., Wang, Q., Piramuthu, R.: Mobile head tracking for ecommerce and beyond. arXiv preprint arXiv:1812.07143 (2018)
Coble, K.: Aitoolbox. a toolbox of AI modules written in swift: Graphs/trees, support vector machines, neural networks, PCA, k-means, genetic algorithms (2017). https://github.com/KevinCoble/AIToolbox
Dodge, R.: Five types of eye movement in the horizontal meridian plane of the field of regard. Am. J. Physiol. Legacy Content 8(4), 307–329 (1903)
Duchowski, A.T.: Eye Tracking Methodology. Theory and Practice. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57883-5
Harrell Jr, F.E., Dupont, C., et al.: HMISC: Harrell miscellaneous. R package version 4.0-3. Online publication (2017)
Hawkeye Labs, I.: Hawkeye access - browse any website, hands-free, all through eye movements(2018). https://apps.apple.com/de/app/hawkeye-access/id1439231627
Hawkeye Labs, I.: Hawkeye - user testing. eye tracking tests on an iphone or ipad, no extra hardware required (2019). https://www.usehawkeye.com/
Hoppe, S., Loetscher, T., Morey, S.A., Bulling, A.: Eye movements during everyday behavior predict personality traits. Front. Hum. Neurosci. 12, 105 (2018)
Hsu, C.L., Chen, Y.C., Yang, T.N., Lin, W.K.: Do website features matter in an online gamification context? focusing on the mediating roles of user experience and attitude. Telematics Inform. 34(4), 196–205 (2017)
Huang, M.X., Li, J., Ngai, G., Leong, H.V.: Screenglint: practical, in-situ gaze estimation on smartphones. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, pp. 2546–2557. ACM (2017)
Khamis, M., Alt, F., Bulling, A.: The past, present, and future of gaze-enabled handheld mobile devices: survey and lessons learned. In: Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services, pp. 1–17 (2018)
Krafka, K., et al.: Eye tracking for everyone. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2176–2184 (2016)
Kytö, M., Ens, B., Piumsomboon, T., Lee, G.A., Billinghurst, M.: Pinpointing: precise head-and eye-based target selection for augmented reality. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2018)
Landwehr, N., Arzt, S., Scheffer, T., Kliegl, R.: A model of individual differences in gaze control during reading. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1810–1815 (2014)
Makowski, S., Jäger, L.A., Abdelwahab, A., Landwehr, N., Scheffer, T.: A discriminative model for identifying readers and assessing text comprehension from eye movements. In: Berlingerio, M., Bonchi, F., Gärtner, T., Hurley, N., Ifrim, G. (eds.) ECML PKDD 2018. LNCS (LNAI), vol. 11051, pp. 209–225. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-10925-7_13
Papoutsaki, A., Laskey, J., Huang, J.: Searchgazer: webcam eye tracking for remote studies of web search. In: Proceedings of the 2017 Conference on Conference Human Information Interaction and Retrieval, pp. 17–26. ACM (2017)
Papoutsaki, A., Sangkloy, P., Laskey, J., Daskalova, N., Huang, J., Hays, J.: Webgazer: scalable webcam eye tracking using user interactions. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence-IJCAI 2016 (2016)
Robinson, D.A.: The mechanics of human smooth pursuit eye movement. J. Physiol. 180(3), 569–591 (1965)
StatCounter: Mobile and tablet internet usage exceeds desktop for first time worldwide. (2016). http://gs.statcounter.com/press/mobile-and-tablet-internet-usage-exceeds-desktop-for-first-time-worldwide
Xu, P., Ehinger, K.A., Zhang, Y., Finkelstein, A., Kulkarni, S.R., Xiao, J.: Turkergaze: Crowdsourcing saliency with webcam based eye tracking. arXiv preprint arXiv:1504.06755 (2015)
Acknowledgment
We appreciate the help of our great team: Danish Ali, Martin Burghart, Tanja Kojic, Luis Meier, Lan Thao Nguyen, Kerstin Pieper, Andres Pinilla, and Sonia Sobol.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Greinacher, R., Voigt-Antons, JN. (2020). Accuracy Assessment of ARKit 2 Based Gaze Estimation. In: Kurosu, M. (eds) Human-Computer Interaction. Design and User Experience. HCII 2020. Lecture Notes in Computer Science(), vol 12181. Springer, Cham. https://doi.org/10.1007/978-3-030-49059-1_32
Download citation
DOI: https://doi.org/10.1007/978-3-030-49059-1_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-49058-4
Online ISBN: 978-3-030-49059-1
eBook Packages: Computer ScienceComputer Science (R0)