Microsoft Technology Licensing, LLC

Christopher Maurice Mei

David Nister

Djordje Nijemcevic

Gerhard A Schneider

Milan Vugdelija

Christopher Charles Aholt

Paulo Ricardo Dos Santos Mendonca

Rayna Demaster-Smith

Chandra Sekhar Gatla

David Douglas Bohn

Scott Charles McEldowney

Karlton David Powell

Derek L Knee

Nathan Ackerman

Vaibhav Thukral

Quentin Simon Charles Miller

Matthew Alastair Johnson

Lev Cherkashin

Steve Robbins

Xinye Lou

John Eldridge

Tadas Baltrusaitis

Bill Chau

Mudit Agrawal

Stephan Joachim Garbin

Marek Adam Kowalski

Virginia Estellers Casas

Robert W Havlik

Jaeyoun Kim

Daniel Stephen Wilde

Ronald Boskovic

Chris Aholt

Paul Malcolm McIlroy

Marta Malgorzata Wilczkowiak

Nguyen Bach

David Zachris Nister

Scott C Mceldowney

The disclosed techniques provide enhanced eye tracking systems utilizing joint estimation of biological parameters and hardware parameters. A system uses joint estimation of biological parameters, e.g., direction and position of an eye, with concurrent estimation of hardware parameters, e.g., camera position or camera direction, to self-calibrate and provide eye tracking estimations to accommodate for deformations and other changes of a device. Sensor data is used to select hardware parameters of a camera for use in the joint estimation with the biological parameters, where the hardware parameters are estimated based on glint and pupil position of a user. The disclosed techniques include a method to model changes of a device, as well as detect and compensate for them while the eye-tracking device is in normal use, without requiring a factory-calibration procedure to be repeated.

Enhanced eye tracking systems utilizing joint biological and hardware estimations

In various examples there is an apparatus for computing an image depicting a face of a wearer of a head mounted display (HMD), as if the wearer was not wearing the HMD. An input image depicts a partial view of the wearer's face captured from at least one face facing capture device in the HMD. A machine learning apparatus is available which has been trained to compute expression parameters from the input image. A 3D face model that has expressions parameters is accessible as well as a photorealiser being a machine learning model trained to map images rendered from the 3D face model to photorealistic images. The apparatus computes expression parameter values from the image using the machine learning apparatus. The apparatus drives the 3D face model with the expression parameter values to produce a 3D model of the face of the wearer and then renders the 3D model from a specified viewpoint to compute a rendered image. The rendered image is upgraded to a photorealistic image using the photorealiser.

Computing images of head mounted display wearer

A computer-implemented method of field calibrating a device is disclosed. In one example, a reference image is acquired, via an infrared camera, while the device is in a factory-calibrated state. The reference image measures a factory-calibrated spatial relationship of one or more infrared-visible fiducial markers of the device relative to the infrared camera while the device is in the factory-calibrated state. Later, a field image is acquired, via the infrared camera. The field image measures an updated spatial relationship of the one or more infrared-visible fiducial markers relative to the infrared camera. The device is field calibrated based on the reference image and the field image.

Fiducial marker based field calibration of a device

Disclosed are an apparatus and a method of low-latency, low-power eye tracking. In some embodiments, the eye tracking method operates a first sensor having a first level of power consumption that tracks positions of an eye of a user. In response to detection that the eye does not change position for a time period, the method stops operation of the first sensor and instead operates a second sensor that detects a change of position of the eye. The second sensor has a level of power consumption lower than the level of power consumption of the first sensor. Once the eye position changes, the second sensor resumes operation.

Eye tracking using video information and electrooculography information

A method of detecting eye location for a head-mounted display system includes directing positioning light to an eye of a user and detecting the positioning light reflected from the eye of the user. The method further includes determining a distance between the eye and a near-eye optic of the head-mounted display system based on attributes of the detected positioning light, and providing feedback for adjusting the distance between the eye and the near-eye optic.

Feedback for optic positioning in display devices

Examples are disclosed herein that relate to user authentication. One example provides a biometric identification system comprising an iris illuminator, an image sensor configured to capture light reflected from irises of a user as a result of those irises being illuminated by the iris illuminator, a drive circuit configured to drive the iris illuminator in a first mode and a second mode that each cause the irises to be illuminated differently, the first and second modes thereby yielding a first mode output at the image sensor and a second mode output at the image sensor, respectively, and a processor configured to process at least one of the first mode output and the second mode output and, in response to such processing, select one of the first mode and the second mode for use in performing an iris authentication on the user.

User authentication using multiple capture techniques

Embodiments that relate to determining an estimated pupil region of an eye are disclosed. In one embodiment a method includes receiving an image of an eye, with the image comprising a plurality of pixels. A rough pupil region is generated using at least a subset of the plurality of pixels. A plurality of pupil boundary point candidates are extracted from the rough pupil region, with each of the candidates weighted based on color values of at least two neighbor pixels. A parametric curve may be fitted to the weighted pupil boundary point candidates to determine the estimated pupil region of the eye of the user.

Pupil detection

Embodiments relating to determining characteristics of eyeglass lenses are disclosed. A head-mounted display device comprises a camera communicatively coupled to a computing device and including an optical axis having a center point. Light sources are configured to emit light rays toward the lens to produce lens glints. The light sources are in a light source plane that is spaced from a lens plane by an offset distance of between 8 mm and 12 mm. The light sources are either spaced vertically from a line perpendicular to the light source plane and extending through the center point by a distance between 13 mm and 53 mm, or spaced horizontally from the line by a distance of between 13 mm and 80 mm. Lens characterization program logic identifies an image location of each lens glint, and outputs an estimated lens shape model comprising the one or more lens characteristics.

Determining lens characteristics

The technology disclosed herein provides various embodiments for mapping glints that reflect off from an object to light sources responsible for the glints. Embodiments disclosed herein are able to correctly map glints to light sources by capturing just a few images with a camera. Each image is captured while illuminating the object with a different pattern of light sources. A glint free image may also be determined. A glint free image is one in which the glints have been removed by image processing techniques.

Mapping glints to light sources

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Seattle, WA, United States of America

Christopher Maurice Mei