Abstract: An imaging device is configured to perform eye tracking. The imaging device comprises an image sensor and an image controller. The image sensor is configured to capture an image of a portion of a user's face. The image controller is configured to: in a first-access step, read out a set of sample pixels, from the image sensor to main memory; identify a pixel of interest from the set of sample pixels and a location of the pixel of interest; in a second-access step, read out remaining pixels in a photodetector group of interest corresponding to the location of the pixel of interest, from the image sensor to the main memory; generate a high-resolution region of interest by combining the pixel of interest and the remaining pixels in the photodetector group; and perform gaze estimation using the full resolution region of interest.

Abstract: A method for color-based calibration for eye-tracking includes presenting a calibration image to an eyebox area via a waveguide of a head mounted display (HMD). An estimated pupil center location of an eye is determined with an eye-tracking system. Next, user input is received in response to the calibration image. The user input includes an indication of a perceived color nonuniformity of the calibration image. The estimated pupil center location is then adjusted based on the user input.

Abstract: In one embodiment, a method for eye-tracking comprises capturing images of a user using one or more cameras, the captured images of the user depicting at least an eye of the user, storing the captured images of the user in a storage device, reading, from the storage device, a down-sampled version of the captured images of the user, detecting one or more first segments in the down-sampled version of the captured images by processing the down-sampled version of the captured images using a machine-learning model, the one or more first segments comprising features of the eye of the user, reading, from the storage device, one or more second segments in the captured images corresponding to the one or more first segments in the down-sampled version of the captured images, and computing a gaze of the user based on the one or more second segments in the captured images.

Abstract: In one embodiment, a method for eye-tracking comprises capturing images of a user using one or more cameras, the captured images of the user depicting at least an eye of the user, storing the captured images of the user in a storage device, reading, from the storage device, a down-sampled version of the captured images of the user, detecting one or more first segments in the down-sampled version of the captured images by processing the down-sampled version of the captured images using a machine-learning model, the one or more first segments comprising features of the eye of the user, reading, from the storage device, one or more second segments in the captured images corresponding to the one or more first segments in the down-sampled version of the captured images, and computing a gaze of the user based on the one or more second segments in the captured images.

Abstract: Systems and methods for robust biometric applications using a detailed eye shape model are described. In one aspect, after receiving an eye image of an eye (e.g., from an eye-tracking camera on an augmented reality display device), an eye shape (e.g., upper or lower eyelids, an iris, or a pupil) of the eye in the eye image is calculated using cascaded shape regression methods. Eye features related to the estimated eye shape can then be determined and used in biometric applications, such as gaze estimation or biometric identification or authentication.

Abstract: A hybrid cornea and pupil eye-tracking technique includes receiving iris-backscattered light from an eye and determining a pupil-position of the eye based on the iris-backscattered light. Cornea-reflected light is received from the eye and an eye-tracking position of the eye is determined based on the cornea-reflected light and the pupil-position.

Abstract: A method for analyzing time series data to identify an event of interest is provided. The method includes receiving, at a computing device, time series data that includes the event of interest, identifying, using the computing device, a start time of the event of interest and an end time of the event of interest by modeling at least one transitional pattern in the time series data, and categorizing, using the computing device, the event of interest based on the at least one transitional pattern.

Abstract: Systems and methods for robust biometric applications using a detailed eye shape model are described. In one aspect, after receiving an eye image of an eye (e.g., from an eye-tracking camera on an augmented reality display device), an eye shape (e.g., upper or lower eyelids, an iris, or a pupil) of the eye in the eye image is calculated using cascaded shape regression methods. Eye features related to the estimated eye shape can then be determined and used in biometric applications, such as gaze estimation or biometric identification or authentication.

Abstract: Systems and methods for robust biometric applications using a detailed eye shape model are described. In one aspect, after receiving an eye image of an eye (e.g., from an eye-tracking camera on an augmented reality display device), an eye shape (e.g., upper or lower eyelids, an iris, or a pupil) of the eye in the eye image is calculated using cascaded shape regression methods. Eye features related to the estimated eye shape can then be determined and used in biometric applications, such as gaze estimation or biometric identification or authentication.

Abstract: Systems and methods for robust biometric applications using a detailed eye shape model are described. In one aspect, after receiving an eye image of an eye (e.g., from an eye-tracking camera on an augmented reality display device), an eye shape (e.g., upper or lower eyelids, an iris, or a pupil) of the eye in the eye image is calculated using cascaded shape regression methods. Eye features related to the estimated eye shape can then be determined and used in biometric applications, such as gaze estimation or biometric identification or authentication.

Abstract: A method for analyzing time series data to identify an event of interest is provided. The method includes receiving, at a computing device, time series data that includes the event of interest, identifying, using the computing device, a start time of the event of interest and an end time of the event of interest by modeling at least one transitional pattern in the time series data, and categorizing, using the computing device, the event of interest based on the at least one transitional pattern.

Abstract: A method for analyzing time series data to identify an event of interest is provided. The method includes receiving, at a computing device, time series data that includes the event of interest, identifying, using the computing device, a start time of the event of interest and an end time of the event of interest by modeling at least one transitional pattern in the time series data, and categorizing, using the computing device, the event of interest based on the at least one transitional pattern.

Abstract: A method includes receiving an image comprising a human head from an imaging device. Further, the method includes grouping one or more pixels of the image into one or more super pixels. Furthermore, the method includes generating a polar coordinate grid for the image, wherein the polar coordinate grid comprises one or more grid points. Additionally, the method includes labeling the one or more grid points as one of a face region, a hair region, and a background region based on tiered structure constraint. The method also includes labeling the one or more super pixels as one of the face region, the hair region, and the background region based on the one or more labels of the one or more grid points. Further, the method includes transmitting a notification comprising the one or more labels of the one or more super pixels.

Abstract: A method includes receiving an image comprising a human head from an imaging device. Further, the method includes grouping one or more pixels of the image into one or more super pixels. Furthermore, the method includes generating a polar coordinate grid for the image, wherein the polar coordinate grid comprises one or more grid points. Additionally, the method includes labeling the one or more grid points as one of a face region, a hair region, and a background region based on tiered structure constraint. The method also includes labeling the one or more super pixels as one of the face region, the hair region, and the background region based on the one or more labels of the one or more grid points. Further, the method includes transmitting a notification comprising the one or more labels of the one or more super pixels.

Abstract: A method for analyzing time series data to identify an event of interest is provided. The method includes receiving, at a computing device, time series data that includes the event of interest, identifying, using the computing device, a start time of the event of interest and an end time of the event of interest by modeling at least one transitional pattern in the time series data, and categorizing, using the computing device, the event of interest based on the at least one transitional pattern.

Abstract: A system and method for imaging a subject are disclosed. A plurality of edge points corresponding to a set of candidate structures are determined in each image frame in a plurality of 3D image frames corresponding to a volume in the subject. A target structure is detected from the set of candidate structures by applying constrained shape fitting to the edge points in each image frame. A subgroup of image frames including the target structure is identified from the 3D frames. A subset of edge points corresponding to the target structure is determined in each of the subgroup of image frames. A plurality of 2D scan planes corresponding to the subset of edge points is determined, and ranked using a determined ranking function to identify a desired scan plane. A diagnostic parameter corresponding to the target structure is measured using a selected image frame that includes the desired scan plane.