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Non-intrusive Calibration for User Interaction based Gaze Estimation

  • Journal of Software Assessment and Valuation
  • Abbr : JSAV
  • 2020, 16(1), pp.45-53
  • DOI : 10.29056/jsav.2020.06.06
  • Publisher : Korea Software Assessment and Valuation Society
  • Research Area : Engineering > Computer Science
  • Received : May 17, 2020
  • Accepted : June 19, 2020
  • Published : June 30, 2020

YongSoo Choi 1 Jang-Hee Yoo 2

1과학기술연합대학원대학교 ICT전공
2한국전자통신연구원

Candidate

ABSTRACT

In this paper, we describe a new method for acquiring calibration data using a user interaction process, which occurs continuously during web browsing in gaze estimation, and for performing calibration naturally while estimating the user's gaze. The proposed non-intrusive calibration is a tuning process over the pre-trained gaze estimation model to adapt to a new user using the obtained data. To achieve this, a generalized CNN model for estimating gaze is trained, then the non-intrusive calibration is employed to adapt quickly to new users through online learning. In experiments, the gaze estimation model is calibrated with a combination of various user interactions to compare the performance, and improved accuracy is achieved compared to existing methods.

Citation status

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