PopEYE is a specialized dataset of 14,976 near-infrared (NIR) ocular images designed to support the development and benchmarking of computer vision algorithms for ophthalmic applications. The dataset focuses on two primary tasks: eye-state detection (open vs. closed) and coarse gaze-direction classification. Context and Motivation In clinical ophthalmic examinations (such as Pupillary Light Reflex measurement or Optical Coherence Tomography), patient cooperation and correct eye positioning are critical for data integrity. PopEYE_2022 was developed to train machine learning models capable of real-time monitoring of the eye status, ensuring that only valid frames are processed and providing immediate feedback on patient alignment. Technical Specifications Imaging Modality: Near-Infrared (NIR) imaging. Optical Setup: Captured using a custom-built Maxwellian-view ophthalmic stimulator, as described in Gibertoni et al. (SPIE 2022). Image Resolution: 772 × 520 pixels (grayscale, PNG format). Dataset Size: ~3.66 GB. Participants: Data collected from 22 different subjects across multiple acquisition sessions to ensure variability in iris patterns, eyelid shapes, and eyelash occlusions. Dataset Structure and Classes The images are organized into six mutually exclusive classes based on the eye's state and positioning: Correct (8,160 images): Eye open, centered, and correctly positioned for measurement. Closed (1,790 images): Full eye closure (blinks or sustained closure). Right (1,336 images): Gaze directed rightward. Down (1,015 images): Gaze directed downwards. Left (1,296 images): Gaze directed leftward. Up (1,379 images): Gaze directed upwards. Key Challenges for AI Models The dataset intentionally includes common NIR artifacts to test model robustness, such as: Specular reflections: Bright spots from NIR LED sources. Partial occlusions: Eyelids and eyelashes obscuring the pupil/limbus boundary. Anatomical variability: Differences in eye shape and iris pigmentation under NIR. Related Publications This dataset has been utilized and validated in the following research works: Sensors 2023 (Vol. 23, Issue 1, 386): Comparative analysis of ML, DL, and Expert Systems for eye classification. SPIE Ophthalmic Technologies XXXV (2025): Real-time monitoring using SVM-based architectures.