<ul><li>Accurate measurement of eyelid parameters such as MRD1, MRD2, and LF is limited by manual methods.</li><li>Deep learning models, including DINOv2, are evaluated for automating these measurements using smartphone-acquired images.</li><li>DINOv2, pretrained through self-supervised learning, demonstrates scalability and robustness, especially under frozen conditions ideal for mobile deployment.</li><li>Enhancements such as focal loss, orthogonal regularization, and binary encoding strategies improve generalization and prediction accuracy of DINOv2.</li></ul>

Training Frozen Feature Pyramid DINOv2 for Eyelid Measurements with Infinite Encoding and Orthogonal Regularization

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