Korean Journal of Artificial Intelligence (한국인공지능학회지)

Korea Artificial Intelligence Association (한국인공지능학회)
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Development of a Non-contact Input System Based on User's Gaze-Tracking and Analysis of Input Factors

  • Jiyoung LIM (Dept. of Computer Software, Korean Bible University) ;
  • Seonjae LEE (Dept. of Computer Software, Korean Bible University) ;
  • Junbeom KIM (Dept. of Computer Software, Korean Bible University) ;
  • Yunseo KIM (Dept. of Computer Software, Korean Bible University) ;
  • Hae-Duck Joshua JEONG (Dept. of Computer Software, Korean Bible University)
  • Received : 2023.02.26
  • Accepted : 2023.03.04
  • Published : 2023.03.30
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Abstract

As mobile devices such as smartphones, tablets, and kiosks become increasingly prevalent, there is growing interest in developing alternative input systems in addition to traditional tools such as keyboards and mouses. Many people use their own bodies as a pointer to enter simple information on a mobile device. However, methods using the body have limitations due to psychological factors that make the contact method unstable, especially during a pandemic, and the risk of shoulder surfing attacks. To overcome these limitations, we propose a simple information input system that utilizes gaze-tracking technology to input passwords and control web surfing using only non-contact gaze. Our proposed system is designed to recognize information input when the user stares at a specific location on the screen in real-time, using intelligent gaze-tracking technology. We present an analysis of the relationship between the gaze input box, gaze time, and average input time, and report experimental results on the effects of varying the size of the gaze input box and gaze time required to achieve 100% accuracy in inputting information. Through this paper, we demonstrate the effectiveness of our system in mitigating the challenges of contact-based input methods, and providing a non-contact alternative that is both secure and convenient.

Keywords

Acknowledgement

This study was supported by UISP (University Innovation Support Project) of Korean Bible University in 2022

References

  1. Boyko, N., Basystiuk, O., & Shakhovska, N. (2018, August). Performance evaluation and comparison of software for face recognition, based on dlib and opencv library. In 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), 478-482.
  2. De Luca, A., Weiss, R., & Drewes, H. (2007, November). 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, 199-202.
  3. Heo, S. Y., Kim, K. M., & Lee, W. J. (2021). Design and Implementation of Visitor Access Control System using Deep learning Face Recognition. Journal of digital convergence, 19(2), 245-251. https://doi.org/10.14400/JDC.2021.19.2.245
  4. Jacob, R. J. (1995). Eye tracking in advanced interface design. Virtual environments and advanced interface design, 258, 288.
  5. Ju, S. H., & Seo, H. S. (2011). Password based user authentication methodology using multi-input on multi-touch environment. Journal of the Korea Society For Simulation, 20(1), 39-49. https://doi.org/10.9709/JKSS.2011.20.1.039
  6. Ju, S. H., & Seo, H. S. (2012). A study on User Authentication Technology of Numeric based Pattern Password. Journal of the Korea society of computer and information, 17(9), 65-73. https://doi.org/10.9708/jksci/2012.17.9.065
  7. Kang, M., Kim, B., Seo, J., Kim, K. (2021). A Study on the Feasibility of IoT and AI-based elderly care system application. Korean Journal of Artificial Intelligence,9(2), 15-21.
  8. Kim, S. Y., & Kwon, T. (2016). A Case Study of Password Usage for Domestic Users. Journal of the Korea Institute of Information Security & Cryptology, 26(4), 961-972. https://doi.org/10.13089/JKIISC.2016.26.4.961
  9. Lee, S. H. (2022). An Input Method for Decimal Password Based on Eyeblink Patterns. Journal of the Korea Institute of Information and Communication Engineering, 26(5), 656-661.
  10. Lim, S. M., Kim, H. J., & Kim, S. K. (2012). Designing Password Input System Resistant on Shoulder Surfing Attack with Statistical Analysis. Journal of the Institute of Electronics and Information Engineers, 49(9), 215-224. https://doi.org/10.5573/ieek.2012.49.9.215
  11. Martin, T. (2012). js-objectdetect. Retrieved Januray 13, 2023, from https://github.com/mtschirs/js-objectdetect
  12. Mathias, A. (2014). clmtrackr. Retrieved Januray 13, 2023, from https://github.com/auduno/clmtrackr
  13. Papoutsaki, A., Laskey, J., & Huang, J. (2017, March). Searchgazer: Webcam eye tracking for remote studies of web search. In Proceedings of the 2017 conference on conference human information interaction and retrieval, 17-26.
  14. Park, K. N. (2019). A morphing method using control lines of facial landmarks. The Journal of Digital Contents Society, 20(2), 443-450. https://doi.org/10.9728/dcs.2019.20.2.443
  15. Puttemans, S. (2021). Object Detection using Haar cascades. Retrieved Januray 12, 2023, from https://docs.opencv.org/3.4/db/d28/tutorial_cascade_classifier.html
  16. Seo, E. S. (2016). mobile eye tracker and for use of the same for revitalizing studies on eye tracking. The Journal of the Korea Contents Association, 16(12), 10-18. https://doi.org/10.5392/JKCA.2016.16.12.010