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Updated: Oct 2, 2025

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Video-Based Eye Blink Identification and Classification.

George Nousias, Eirini-Kanella Panagiotopoulou, Konstantinos Delibasis

    IEEE Journal of Biomedical and Health Informatics
    |February 25, 2022
    PubMed
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    This study introduces an automated system for detecting and classifying complete and incomplete blinks using deep learning. The AI system accurately analyzes facial images, outperforming human experts in blink classification for clinical insights.

    Area of Science:

    • Ophthalmology
    • Neurology
    • Computer Vision
    • Biomedical Engineering

    Background:

    • Blink detection and classification are crucial clinical indicators linked to neurological and ophthalmological conditions.
    • Existing methods may lack the precision required for detailed clinical analysis of blink types.

    Purpose of the Study:

    • To develop and validate an automated system for detecting and classifying complete and incomplete blinks.
    • To utilize deep learning for high-resolution facial image analysis during clinical examinations.

    Main Methods:

    • A deep learning encoder-decoder neural architecture (DeepLabv3+) was employed to segment irises and eyelids.
    • Palpebral fissure height and iris diameter were calculated from segmented images.

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  • Temporal filtering and adaptive thresholding were used to classify blink types for each eye.

    • Main Results:

    • The system achieved high F1-scores: 95.3% for complete blinks and 80.9% for incomplete blinks.
    • The automated system outperformed three independent human experts in blink classification accuracy.
    • The method demonstrated robustness against facial movements, spectacles, and facemask obstructions.

    Conclusions:

    • The proposed deep learning system provides an accurate and robust method for automated blink detection and classification.
    • This technology offers a valuable tool for clinical assessment related to neurological and ophthalmological disorders.
    • The system's performance surpasses human expert capabilities, suggesting potential for widespread clinical adoption.