Microsoft Technology Licensing, LLC

Jatin Sharma

Jay Beavers

Jonathan Campbell

Peter John Ansell

Kenneth Paul Hinckley

Michel Pahud

Systems and methods are provided for collecting eye-gaze data for training an eye-gaze prediction model. The collecting includes selecting a scan path passing through a series of regions of a grid on a screen of a computing device, moving a symbol as an eye-gaze target along the scan path, and receiving facial images at eye-gaze points. The eye-gaze points are uniformly distributed within the respective regions. Areas of the regions that are adjacent to edges and corners of the screen are smaller than other regions. The difference in areas shifts centers of the regions toward the edges, density of data closer to the edges. The scan path passes through locations in proximity to the edges and corners of the screen for capturing more eye-gaze points in the proximity. The methods interactively enhance variations of facial images by displaying instructions to the user to make specific actions associated with the face.

Systems and methods of capturing eye-gaze data

Aspects of the present disclosure relate to multi-user, multi-device gaze tracking. In examples, a system includes at least one processor, and memory storing instructions that, when executed by the at least one processor, causes the system to perform a set of operations. The set of operations include identifying a plurality of computing devices, and identifying one or more users. The set of operations may further include receiving gaze input data and load data, from two or more of the plurality of computing devices. The set of operations may further include performing load balancing between the plurality of devices, wherein the load balancing comprises assigning one or more tasks from a first of the plurality of computing devices to a second of the plurality of computing devices based upon the gaze input data.

Multi-device gaze tracking

Systems and methods are provided for predicting an eye gaze location of an operator of a computing device. In particular, the method generates an image grid that includes regions of interest based on a facial image. The facial image is based on a received image frame of a video stream that captures the operator using the computing device. The image grid further includes a region that indicate rotation information of the face. The method further uses a combination of trained neural networks to extract features of the regions of interest in the image grid and predict the eye gaze location on the screen of the computing device. The trained set of neural networks includes a convolutional neural network. The method optionally generate head pose pitch, roll, and yaw information to improve accuracy of predicting the location of an eye gaze.

Systems and methods for gaze-tracking

Systems and methods are provided for collecting eye-gaze data for training an eye-gaze prediction model. The collecting includes selecting a scan path that passes through a series of regions of a grid on a screen of a computing device, moving a symbol as an eye-gaze target along the scan path, and receiving facial images at eye-gaze points. The eye-gaze points are uniformly distributed within the respective regions. Areas of the regions that are adjacent to edges and corners of the screen are smaller than other regions. The difference in areas shifts centers of the regions toward the edges, density of data closer to the edges. The scan path passes through locations in proximity to the edges and corners of the screen for capturing more eye-gaze points in the proximity. The methods interactively enhance variations of facial images by displaying instructions to the user to make specific actions associated with the face.

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Sammamish, WA, United States of America

Jatin Sharma