Blink Technologies, Inc.

Andrey Zhitnikov

Gilad Drozdov

Oren Haimovitch-Yogev

Almog David

Tsahi Mizrahi

Artyom Borzin

Unsupervised, deep learning of eye-landmarks in a user-specific eyes' image data by capturing an unlabeled image comprising an eye region of a user, using an initial geometrically regularized loss function, training a plurality of convolutional autoencoders on the unlabeled image comprising the eye region of the user to recover a plurality of user-specific eye landmarks, training a convolutional neural network for autoencoded landmarks-based recovery from the unlabeled image, and where the initial geometrically regularized loss function is represented by the formula L=λL+λL+λL+λLwhere Lis total AutoEncoder Loss, λLis λ-weighted reconstruction loss, λLis λ-weighted concentration loss, λLis λ-weighted separation loss, and λLis λ-weighted equivalence loss.

Geometrically constrained, unsupervised training of convolutional autoencoders for extraction of eye landmarks

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.

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Haifa, Israel

Andrey Zhitnikov