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

arXiv: https://arxiv.org/abs/2604.01909 · HTML: https://mlsanity.com/paper/2604.01909/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 24 references that are in the map.
- First posted on arXiv 2 Apr 2026.

## Builds on (most cited first, 3 of 3)

- [OpenEDS2020: Open Eyes Dataset](https://mlsanity.com/paper/2005.03876/) (2020)
- [Precise localization of corneal reflections in eye images using deep learning trained on synthetic data](https://mlsanity.com/paper/2304.05673/) (2023)
- [LEyes: A Lightweight Framework for Deep Learning-Based Eye Tracking using Synthetic Eye Images](https://mlsanity.com/paper/2309.06129/) (2023)

## Abstract (arXiv)

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.