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This paper introduces a reproducible, 2D geometry-driven pipeline for multi-glint detection and matching in pupil-corneal reflection (P-CR) eye tracking. The core innovation is a Similarity-Layout Alignment (SLA) procedure that treats glints as structured constellations, adapting constellation matching to the constraints of multi-LED eye tracking. Evaluation on a public dataset demonstrates stable, identity-preserving correspondence under noisy conditions, with code and evaluation scripts released for reproducibility.
Glint matching in eye tracking gets a major reproducibility boost with a new constellation-based method that treats corneal reflections like stars in the night sky.
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.
