Conference Paper2019Cited by 4

Eye-gaze to screen location mapping for UI evaluation of webpages

Md Sazzad Hossain, Amin Ahsan Ali, M Ashraful Amin

Proceedings of the 3rd International Conference on Graphics and Signal Processing

ACM, pp. 100–104

DOI: 10.1145/3338472.3338483URL

Abstract

This paper presents a way to track eye-gaze by using webcam and mapping the eye-gaze data compensating head pose and orientation on the display screen. First, we have shown a blank screen with red dots to 10 individuals and recorded their eye-gaze pattern and head orientation associated with that screen location by automated annotation. Then, we trained a neural network to learn the relationship between eye-gaze and head pose with screen location. The proposed method can map eye-gazes to screen with 68.3% accuracy. Next, by using the trained model to estimate eye gaze on screen, we have evaluated content of a website. This gives us an automated way to evaluate the UI of a website. The evaluation metric might be used with several other metrics to define a standard for web design and layout. This also gives insight to the likes and dislikes, important areas of a website. Also, eye tracking by only a webcam simplifies the matter to use this technology in various fields which might open the future prospect of enormous applications.

Keywords

  • Eye tracking
  • webcam-based eye tracking
  • user interface evaluation
  • website usability
  • head orientation
  • automatic annotation

CCDS Authors

Amin Ahsan Ali, PhD
Amin Ahsan Ali, PhD

Amin Ahsan Ali, PhD

  • Director, AI/ML Wing

Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

ACL, CHI, UbiComp, ECCV, IJCAI, ECAI, WACV, IPSN, PAKDD, LREC, IJCNN, ICPR, ICIP, BEA, BLP, ICMLA, Scientific Reports, Scientific Data, Pattern Recognition, Information Sciences, Ad Hoc Networks, Drug and Alcohol Dependence, PLOS ONE

Research Interests

Artificial Intelligence, Machine Learning

Prof. M Ashraful Amin, PhD
M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

  • Founder Director, CCDS
  • Director, AI/ML, HCI & DS Wings

Professor

+1 more affiliation

Department of Information Sciences and Technology

George Mason University, USA

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics

References

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  3. 3.Xucong Zhang, Yusuke Sugano, Mario Fritz, Andreas Bulling. (2015). Appearance-Based Gaze Estimation in the Wild.
  4. 4.Tadas Baltrušaitis, Peter Robinson, Louis–Philippe Morency. (2013). Constrained Local Neural Fields for Robust Facial Landmark Detection in the Wild. , 354–361[10.1109/iccvw.2013.54]
  5. 5.Mauricio Villegas, Henning Müller, Andrew Gilbert, Luca Piras, Josiah Wang, Krystian Mikolajczyk, Alba García Seco de Herrera, Stefano Bromuri, M. Ashraful Amin, Mahmood Kazi Mohammed, Burak Acar, Suzan Üsküdarlı, Neda Barzegar Marvasti, José F. Aldana, María del Mar Roldán‐García. (2015). General Overview of ImageCLEF at the CLEF 2015 Labs. Lecture notes in computer science, 444–461[10.1007/978-3-319-24027-5_45]
  6. 6.M. Ashraful Amin, Hong Yan. (2009). AN EMPIRICAL STUDY ON THE CHARACTERISTICS OF GABOR REPRESENTATIONS FOR FACE RECOGNITION. International Journal of Pattern Recognition and Artificial Intelligence, 23(03), 401–431[10.1142/s0218001409007181]
  7. 7.M. Ashraful Amin, Nitin Afzulpurkar, Matthew N. Dailey, Vatcharaporn Esichaikul, Dentcho N. Batanov. (2005). Fuzzy-C-Mean Determines the Principle Component Pairs to Estimate the Degree of Emotion from Facial Expressions. Lecture notes in computer science, 484–493[10.1007/11539506_62]

Cited By

  1. 1.Jakub Štěpán Novák, Jan Masner, Petr Benda, Pavel Šimek, Vojtěch Merunka. (2023). Eye Tracking, Usability, and User Experience: A Systematic Review. International Journal of Human-Computer Interaction, 40(17), 4484–4500[10.1080/10447318.2023.2221600]
  2. 2.Michael Chromik, Florian Lachner, Andreas Butz. (2020). ML for UX? - An Inventory and Predictions on the Use of Machine Learning Techniques for UX Research. , 1–11[10.1145/3419249.3420163]
  3. 3.Maja Kędras, Janusz Sobecki. (2023). What Is Hidden in Clear Sight and How to Find It—A Survey of the Integration of Artificial Intelligence and Eye Tracking. Information, 14(11), 624[10.3390/info14110624]
  4. 4.Alberto J. Molina-Cantero, Clara Lebrato-Vázquez, Juan A. Castro-García, Manuel Merino-Monge, Félix Biscarri-Triviño, José I. Escudero-Fombuena. (2024). A review on visible-light eye-tracking methods based on a low-cost camera. Journal of Ambient Intelligence and Humanized Computing, 15(4), 2381–2397[10.1007/s12652-024-04760-8]
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