Abstract: Methods, systems, and storage media for projecting a viewer-specific 3D object perspectives from a single 3D display are disclosed. Implementations may: acquire face and eye region image data of a plurality of viewers within a field of view of at least one camera associated with a 3D-enabled digital display; analyze the eye region image data to determine at least one 3D eye position, at least one eye state, at least one gaze angle, and at least one point-of-regard for a viewer relative to at least one camera associated with the 3D-enabled digital display; and calculate a plurality of processed image projections for display by the single 3D display. The digital-processing of input image projection enables a separate optical input to the user's eyes, and by the use of visual-acuity pre-processing of the image—via visual-field kernel, enables the treatment of eye abbreviations, including an Amblyopic-eye without the need for any additional eye-ware, or head-up-displays (HMD's).

Abstract: 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 LAE=?reconLrecon+?concLconc+?sepLsep+?eqvLeqv where LAE is total AutoEncoder Loss, ?reconLrecon is ?-weighted reconstruction loss, ?concLconce is ?-weighted concentration loss, ?sepLsep is ?-weighted separation loss, and ?eqvLeqv is ?-weighted equivalence loss.

Abstract: Methods, systems, and storage media for projecting multi-viewer-specific 3D object perspectives from a single 3D display are disclosed. Implementations may: acquire face and eye region image data of a plurality of viewers within a field of view of at least one camera associated with a 3D-enabled digital display; analyze the eye region image data to determine at least one 3D eye position, at least one eye state, at least one gaze angle, and at least one point-of-regard for at least one viewer relative to at least one camera associated with the 3D-enabled digital display; and calculate a plurality of image projections for display by the single 3D display.

Abstract: Methods, systems, and storage media for projecting multi-viewer-specific 3D object perspectives from a single 3D display are disclosed. Implementations may: acquire face and eye region image data of a plurality of viewers within a field of view of at least one camera associated with a 3D-enabled digital display; analyze the eye region image data to determine at least one 3D eye position, at least one eye state, at least one gaze angle, and at least one point-of-regard for at least one viewer relative to at least one camera associated with the 3D-enabled digital display; and calculate a plurality of image projections for display by the single 3D display.

Abstract: Methods, systems, and storage media for projecting multi-viewer-specific 3D object perspectives from a single 3D display are disclosed. Implementations may: acquire face and eye region image data of a plurality of viewers within a field of view of at least one camera associated with a 3D-enabled digital display; analyze the eye region image data to determine at least one 3D eye position, at least one eye state, at least one gaze angle, and at least one point-of-regard for at least one viewer relative to at least one camera associated with the 3D-enabled digital display; and calculate a plurality of image projections for display by the single 3D display.

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: Methods, systems, and storage media for projecting a viewer-specific 3D object perspectives from a single 3D display are disclosed. Implementations may: acquire face and eye region image data of a plurality of viewers within a field of view of at least one camera associated with a 3D-enabled digital display; analyze the eye region image data to determine at least one 3D eye position, at least one eye state, at least one gaze angle, and at least one point-of-regard for a viewer relative to at least one camera associated with the 3D-enabled digital display; and calculate a plurality of processed image projections for display by the single 3D display. The digital-processing of input image projection enables a separate optical input to the user's eyes, and by the use of visual-acuity pre-processing of the image—via visual-field kernel, enables the treatment of eye abbreviations, including an Amblyopic-eye without the need for any additional eye-ware, or head-up-displays (HMD's).

Abstract: A computer implemented method and system for fitting eyeglasses captures images of the consumer while naturally trying-on frames in the normal course of selecting new frames. The system and method capture natural images of frame placement and using gaze prediction and eye tracking techniques ascertain eye measurements for accurate placement of the prescription within new eyewear.

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.