Abstract: Geometrically constrained unsupervised training of convolutional autoencoders on unlabeled images for extracting eye landmarks by capturing an unlabeled image including the eye region of a user and training multiple convolutional autoencoders on the unlabeled image using an initial geometrically regularized loss function to determine multiple eye landmarks.

Abstract: The disclosure relates to systems, methods and programs for geometrically constrained, unsupervised training of convolutional autoencoders on unlabeled images for extracting eye landmarks. Disclosed systems for unsupervised deep learning of gaze estimation in eyes' image data are implementable in a computerized system. Disclosed methods include capturing an unlabeled image comprising the eye region of a user; and training a plurality of convolutional autoencoders on the unlabeled image comprising the eye region of a user using an initial geometrically regularized loss function to determine a plurality of eye landmarks.

Abstract: The disclosure relates to systems, methods and programs for geometrically constrained, unsupervised training of convolutional autoencoders on unlabeled images for extracting eye landmarks.