Efficiency in Real-time Webcam Gaze Tracking. (arXiv:2009.01270v1 [cs.CV])


Efficiency and ease of use are essential for practical applications of camera
based eye/gaze-tracking. Gaze tracking involves estimating where a person is
looking on a screen based on face images from a computer-facing camera. In this
paper we investigate two complementary forms of efficiency in gaze tracking: 1.
The computational efficiency of the system which is dominated by the inference
speed of a CNN predicting gaze-vectors; 2. The usability efficiency which is
determined by the tediousness of the mandatory calibration of the gaze-vector
to a computer screen. To do so, we evaluate the computational speed/accuracy
trade-off for the CNN and the calibration effort/accuracy trade-off for screen
calibration. For the CNN, we evaluate the full face, two-eyes, and single eye
input. For screen calibration, we measure the number of calibration points
needed and evaluate three types of calibration: 1. pure geometry, 2. pure
machine learning, and 3. hybrid geometric regression. Results suggest that a
single eye input and geometric regression calibration achieve the best
trade-off.

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