Efficiency in Real-time Webcam Gaze Tracking
Abstract
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.
Cite
@article{arxiv.2009.01270,
title = {Efficiency in Real-time Webcam Gaze Tracking},
author = {Amogh Gudi and Xin Li and Jan van Gemert},
journal= {arXiv preprint arXiv:2009.01270},
year = {2020}
}
Comments
Awarded Best Paper at European Conference on Computer Vision (ECCV) Workshop on Eye Gaze in AR, VR, and in the Wild (OpenEyes) 2020