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    This novel lensless facial recognition system uses a micro-LED display and deep learning to encrypt facial data, protecting privacy. It achieves high accuracy without retraining the model for new users.

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    Area of Science:

    • Computer Vision
    • Biometrics
    • Optics

    Background:

    • Current facial recognition systems often capture clear facial images, posing privacy risks.
    • Retraining deep learning models for new users in recognition systems is resource-intensive.

    Purpose of the Study:

    • To develop a novel under-display lensless facial recognition system that enhances user privacy.
    • To enable user registration and recognition without model retraining.

    Main Methods:

    • The system integrates a transparent micro-LED display, an amplitude modulation mask, a CMOS sensor, and a deep learning model.
    • Facial information is optically encrypted, rendering it human-incomprehensible at the imaging plane.
    • A deep learning model, repurposed as a feature extractor, analyzes encrypted images in a latent space.

    Main Results:

    • The system achieved 93.02% accuracy, 97.51% precision, and 97.74% specificity.
    • New users can be registered and recognized without retraining the deep learning model.
    • The lensless approach fundamentally protects user privacy by never capturing clear facial features.

    Conclusions:

    • The proposed under-display lensless facial recognition system offers a privacy-preserving solution.
    • The feature extraction method allows for flexible user management without model modification.
    • The system demonstrates stable recognition performance and enhanced security.