Night Eyes: A Reproducible Framework for Constellation-Based Corneal Reflection Matching

2026-04-02

AI Summary

This work addresses the limited cross-hardware reproducibility of corneal glint detection in multi-LED eye tracking, which often relies on system-specific heuristics. The authors propose a novel constellation-matching framework grounded in 2D geometric structure, adapting astronomical star identification principles to treat multiple glints as spatially structured “constellations.” Their approach decouples glint detection from correspondence through a Similarity–Layout Alignment (SLA) pipeline, incorporating controlled over-detection, adaptive candidate fallback, and appearance-aware scoring, further enhanced by semantic layout priors. This design significantly improves robustness and generalization, achieving stable identity-preserving matching under noisy conditions on public multi-LED datasets. To promote transparent reproducibility, the authors release their code, parameters, and evaluation scripts.

Abstract

Corneal reflection (glint) detection plays an important role in pupil-corneal reflection (P-CR) eye tracking, but in practice it is often handled as heuristics embedded within larger systems, making reproducibility difficult across hardware setups. We introduce a 2D geometry-driven, constellation-based pipeline for mulit-glint detection and matching, focusing on reproducibility and clear evaluation. Inspired by lost-in-space star identification, we treat glints as structured constellations rather than independent blobs. We propose a Similarity-Layout Alignment (SLA) procedure which adapts constellation matching to the specific constraints of multi-LED eye tracking. The framework brings together controlled over-detection, adaptive candidate fallback, appearance-aware scoring, and optional semantic layout priors while keeping detection and correspondence explicitly separated. Evaluated on a public multi-LED dataset, the system provides stable identity-preserving correspondence under noisy conditions. We release code, presets, and evaluation scripts to enable transparent replication, comparison, and dataset annotation.

Problem

• corneal reflection
• eye tracking
• reproducibility
• multi-glint detection
• constellation matching

Innovation

• corneal reflection
• constellation matching
• eye tracking
• reproducibility
• Similarity-Layout Alignment