Gaze point detection by computing the 3d positions and 3d motion of face

Kang Ryoung Park, Jaihie Kim

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


Gaze detection is to locate the position on a monitor screen where a user is looking. In our work, we implement it with a computer vision system setting a single camera above a monitor and a user moves (rotates and/or translates) her face to gaze at a different position on the monitor. For our case, the user is requested not to move pupils of her eyes when she gazes at a different position on the monitor screen, though we are working on to relax this restriction. To detect the gaze position, we extract facial features (both eyes, nostrils and lip corners) automatically in 2D camera images. From the movement of feature points detected in starting images, we can compute the initial 3D positions of those features by recursive estimation algorithm. Then, when a user moves her head in order to gaze at one position on a monitor, the moved 3D positions of those features can be computed from 3D motion estimation by Iterative Extended Kaiman Filter (IEKF) and affine transform. Finally, the gaze position on a monitor is computed from the normal vector of the plane determined by those moved 3D positions of features. Especially, in order to obtain the exact 3D positions of initial feature points, we unify three coordinate systems (face, monitor and camera coordinate system) based on perspective transformation. As experimental results, the 3D position estimation error of initial feature points, which is the RMS error between the estimated initial 3D feature positions and the real positions (measured by 3D position tracker sensor) is about 1.28cm (0.75cm in X axis, 0.85cm in Y axis, 0.6cm in Z axis) and the 3D motion estimation errors of feature points by Iterative Extended Kaiman Filter (IEKF) are about 2.8 degrees and 1.21cm in rotation and translation, respectively. From that, we can obtain the gaze position on a monitor (17 inches) and the gaze position accuracy between the calculated positions and the real ones is about 2.06 inches of RMS error.

Original languageEnglish
Pages (from-to)884-894
Number of pages11
JournalIEICE Transactions on Information and Systems
Issue number4
Publication statusPublished - 2000

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence


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