Eye and hand tracking utilizing lensless camera and machine learning

US12242667B2 · kind B2 · utility

Assignee

• MICROSOFT TECHNOLOGY LICENSING, LLC · US

Inventors

• Curtis Tesdahl · Fort Collins, US
• Benjamin Eliot LUNDELL · Seattle, US
• David Rohn · Fort Collins, US
• Dmitry Reshidko · Sammamish, US
• Dmitriy Churin · Redmond, US
• Kevin Matherson · Fort Collins, US
• Sayyed Jaffar Ali RAZA · Fort Collins, US

Key dates

• Filing date: Oct 3, 2023
• Grant date: Mar 4, 2025
• Expiry date: Oct 3, 2043

Classification

• Technology area (CPC G): Physics
• CPC primary: G02B2027/0178
• WIPO field: Optics
• WIPO sector: Instruments

Abstract

Eye and hand tracking systems in head-mounted display (HMD) devices are arranged with lensless camera systems using optical masks as encoding elements that apply convolutions to optical images of body parts (e.g., eyes or hands) of HMD device users. The convolved body images are scrambled or coded representations that are captured by a sensor in the system, but are not human-recognizable. A machine learning system such as a neural network is configured to extract body features directly from the coded representation without performance of deconvolutions conventionally utilized to reconstruct the original body images in human-recognizable form. The extracted body features are utilized by the respective eye or hand tracking systems to output relevant tracking data for the user's eyes or hands which may be utilized by the HMD device to support various applications and user experiences. The lensless camera and machine learning system are jointly optimizable on an end-to-end basis.