Binocular Gaze Estimation with Single Camera and Single Light Source

2026-07-08 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Computer Vision & Pattern Recognition · Depth: Expert, short

Summary

A novel method for binocular gaze estimation, "Binocular Gaze Estimation with Single Camera and Single Light Source," was presented by Tongbing Huang, Yang Fu, Yunfei Wang, and Zhaocan Wang at the 2019 International Conference on Video, Signal and Image Processing (VSIP 2019) in Wuhan, China, and published in its proceedings (pp. 10-14, ACM, 2020). This approach challenges the conventional requirement of one camera and two light sources for free head movement gaze tracking by achieving estimation with only one camera and one light source. The technique introduces a "virtual light source" and its corresponding "virtual glint," which is estimated by exploiting the relationship between pupil and glint distances in the captured image. Gaze is then determined via polynomial regression, adapted for a single-glint system using a newly verified normalization factor. While demonstrating acceptable performance, the method shows some degradation compared to systems utilizing two actual light sources.

Key takeaway

For Computer Vision Engineers designing compact eye-tracking solutions, this method demonstrates that you can achieve binocular gaze estimation using only one camera and one light source. This reduces hardware complexity, making it suitable for mobile devices where component count is critical. You should consider this approach for resource-constrained applications, understanding that it offers acceptable performance with some degradation compared to conventional two-light-source systems.

Key insights

A single-camera, single-light-source system can estimate binocular gaze by simulating a "virtual light source" and "virtual glint."

Principles

• Gaze estimation can be achieved with reduced hardware.
• Virtual elements can compensate for missing physical components.
• Pupil-glint distance relationships are key for virtual glint estimation.

Method

Introduce a "virtual light source" symmetrical to the real one; estimate its "virtual glint" using pupil-glint distance relationships; apply polynomial regression with a new normalization factor for gaze.

In practice

• Design compact eye-tracking systems for mobile devices.
• Reduce component count in gaze tracking hardware.
• Explore virtual sensor techniques for resource-constrained applications.

Topics

• Binocular Gaze Estimation
• Single Camera Systems
• Virtual Light Source
• Polynomial Regression
• Eye Tracking
• Mobile Devices

Best for: AI Scientist, Research Scientist, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.