Facebook Technologies, LLC

Meta Platforms Technologies, LLC

Jixu Chen

Jeffrey Hung Wong

Robin Sharma

Karol Constantine Hatzilias

Sebastian Sztuk

Javier San Agustin Lopez

Martin Henrik Tall

Kun Liu

Thomas Scott Murdison

Xu Dong

Taehee Cho

Kapil Krishnakumar

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.

Region of interest sampling and retrieval for artificial reality systems

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.

Color-based calibration for eye-tracking

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.

Distributed sensor module for eye-tracking

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.

Hybrid cornea and pupil tracking

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Redwood City, CA, United States of America

Jixu Chen