Blink Technologies, Inc.

Blink O.g. Ltd.

Blonk O.g. Ltd.

Gilad Drozdov

Oren Haimovitch-Yogev

Tsahi Mizrahi

Almog David

Uri Wollner

Artyom Borzin

Igor Nor

Andrey Zhitnikov

Nadav Yehonatan Arbel

Tal Knafo

Oren Haimovitch Yogev

Soliman Nasser

Nadav Arbel

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.

System and method for fitting eye wear

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).

System and method for the diagnosis and treatment of amblyopia using a 3D display

The disclosure relates to systems, methods and programs implicitly determines the individual gaze correction required for each user when it comes to determining the point-of-regard and region of interest using a seamless-adaptation-process (SAP) in settings when calibration is not practical/desired (e.g. long-range display eye sensing). The present systems, methods, and programs also correct for variables arising from salient features such as user eye attributes (e.g., ocular dominance or chronic eyesight conditions) that are not visible within a digital frame, environmental conditions, and camera-screen settings. The method is not eye-tracking method dependent, can perform under all environmental conditions while the displayed content is known. The concept of a data driven seamless gaze correction can be extended beyond long-range displays to any gaze-enabled device where fixation accuracy can be improved (e.g., mobile phones, tablets etc.).

Personalized calibration-independent regional fixation prediction for simultaneous users of a digital display

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 developing real-time user-specific eye model based on iris localization using solely pupil-ellipse analysis.

Pupil ellipse-based, real-time iris localization

Embodiments described systems and method to estimate user gaze comprising receiving a target image data associated with the user from a camera coupled with the display, wherein the target image data includes a target eye patch image data associated with the user; and determining, using a neural network, the target point of regard associated with the target image data based on: the target eye patch image data, a plurality of aggregated gaze reference vectors, and a plurality of reference image data associated with the user, respectively associated with the plurality of aggregated gaze reference vectors. In embodiments, the target point of regard is determined within a predetermined threshold. In embodiments, the target point of regard is mapped onto the display.

Gaze estimation systems and methods using relative points of regard

Anatomically-constrained gaze estimation providing a point of reference (PoR) in a 3D field and/or 2D plane, based on 6 DOF head pose constrained by the eyeball center being fixed in a common 3D coordinate system.

Systems and methods for anatomy-constrained gaze estimation

The disclosure relates to systems, methods, and programs for implicitly determining the relation of the eyes associated with gaze inference, including salient features (e.g., ocular dominance) that are not visible within a digital frame, allowing for real-time determination of the user's dominant eye for the purpose of gaze estimation, and point-of-regard mapping onto a 2D plane, achieved through an end-to-end training of an eye selector agent with domain-expertise knowledge embedded in an unsupervised manner via a convolutional deep neural network training process.

Appearance based dominant eye selector systems and methods for gaze estimation from passive imagery

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.

The disclosure relates to systems, methods and programs for anatomically-constrained gaze estimation. More specifically, the disclosure is directed to systems, methods and programs for providing a point of reference (PoR) in a 3D field and/or 2D plane, based on 6 DOF head pose constrained by the eyeball center being fixed in a common 3D coordinate system.

Growing community of inventors

Haifa, Israel

Gilad Drozdov