Abstract: 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.

Abstract: 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.

Abstract: 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.).

Abstract: 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.

Abstract: 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.

Abstract: 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.

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