Binocular Gaze Estimation with Single Camera and Single Light Source

2026-07-06 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

A novel binocular gaze estimation method is proposed, utilizing only one camera and one physical light source, contrary to the commonly accepted requirement of two light sources. This approach introduces a "virtual light source" positioned symmetrically to the real one, which generates a "virtual glint" in the captured image. The system estimates this virtual glint by analyzing the relationship between the distances of two pupils and two glints. Gaze is then estimated using polynomial regression, under the assumption that two light sources are present. The method also verifies a new normalization factor specifically for one-glint systems, proving its practicality. While the performance is deemed acceptable, a degradation is observed when compared to systems employing two actual light sources, making it suitable for component-constrained scenarios like mobile devices.

Key takeaway

For Computer Vision Engineers designing eye-tracking systems for mobile or embedded devices, this research offers a viable path to reduce hardware complexity. You can achieve binocular gaze estimation with only one camera and one light source, significantly lowering component costs and integration challenges. Be aware that this approach introduces some performance degradation compared to traditional two-light source setups, requiring careful evaluation for your specific application's accuracy needs.

Key insights

The paper demonstrates binocular gaze estimation with one camera and one light source by introducing a "virtual glint" for regression.

Principles

• Gaze estimation can be achieved with fewer components.
• Virtual glints can simulate missing hardware.
• Normalization factors are crucial for one-glint systems.

Method

The method introduces a "virtual light source" to create a "virtual glint," estimated via pupil-glint distance relationships. Gaze is then estimated using polynomial regression, assuming two light sources, with a new normalization factor.

In practice

• Implement gaze tracking on mobile devices.
• Reduce hardware components for eye tracking.
• Apply polynomial regression with virtual glints.

Topics

• Gaze Estimation
• Binocular Vision
• Computer Vision
• Mobile Eye Tracking
• Virtual Glint
• Polynomial Regression

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

Related on AIssential

• An Extensible and Lightweight Unified Architecture for Demosaicing Pixel-bin Image Sensors — A unified, lightweight architecture with CFA identification improves pixel-bin sensor demosaicing quality and efficiency.
• ST-DiffEye: Diffusion-based Continuous Gaze Generation via Joint Scanpath-Trajectory Modeling — ST-DiffEye jointly models gaze trajectories and scanpaths via diffusion to generate diverse, accurate human gaze patterns.
• Multi-scale interaction network for stereo image super-resolution — A multi-scale interaction network enhances stereo image super-resolution by optimizing intra-view and cross-view information exploitation.
• Muybridge raises $16M Series A to scale software-defined camera technology — Software-defined imaging platforms can replace traditional broadcast camera infrastructure with real-time virtual perspectives.
• Illumination-Robust Camera-Based Heart-Rate Estimation for Physiological Sensing in Robots — Robust remote photoplethysmography (rPPG) for robots is achievable despite illumination variations.
• Pixel Cube: Diffusion-based Portrait Video Relighting Through Realistic Lighting Reproduction — Diffusion models, trained on hybrid real/synthetic data from a specialized LED stage, enable photorealistic, temporally consistent portrait video relighting.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.