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 Kalman 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 1.28 cm (0.75 cm in X axis, 0.85 cm in Y axis, 0.6 cm in Z axis) and the 3D motion estimation errors of feature points by Iterative Extended Kalman Filter (IEKF) are about 2.8 degrees and 1.21 cm 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.

Indexing techniques usually are used in model-based object recognition and ray tracing algorithms. In this paper we present a new method for estimating the three-dimensional position of a subject (resp. object) in a circumscribed space based on an indexing method. We construct two and three-dimensional indices of a space, which are used to estimate the three-dimensional position by an interpolation technique. There are two processes in estimating the three-dimensional position of a subject (resp. object): preprocessing and three-dimensional position estimation. We have implemented this idea using stereo camera, and tested by using two different sizes of a grid pattern. Promising results for preprocessing and 3D position estimation are presented. Moreover, we show that this approach can also be extended for multiple cameras.