# Efficiency in Real-time Webcam Gaze Tracking

arXiv: https://arxiv.org/abs/2009.01270 · HTML: https://mlsanity.com/paper/2009.01270/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2020.

## Built on by (most cited first, 2 of 2)

- [3D Prior is All You Need: Cross-Task Few-shot 2D Gaze Estimation](https://mlsanity.com/paper/2502.04074/) (2025)
- [Deployment-Oriented Session-wise Meta-Calibration for Landmark-Based Webcam Gaze Tracking](https://mlsanity.com/paper/2603.12388/) (2026)

## Abstract (arXiv)

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.