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

TL;DR

This paper presents a reproducible, constellation-based framework for corneal reflection detection in eye tracking, emphasizing geometric matching, robustness, and transparent evaluation.

Contribution

It introduces a novel constellation-based pipeline with SLA matching, enhancing reproducibility and robustness in multi-glint detection across hardware setups.

Findings

Provides stable, identity-preserving correspondence under noisy conditions.

Achieves reproducible detection with explicit separation of detection and matching.

Offers open-source code and evaluation tools for transparency.

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.