Apple inc. (20240104967). Synthetic Gaze Enrollment simplified abstract
Contents
- 1 Synthetic Gaze Enrollment
Synthetic Gaze Enrollment
Organization Name
Inventor(s)
Rene Heideklang of Neuenhagen bei Berlin (DE)
Tom Sengelaub of Oakland CA (US)
Synthetic Gaze Enrollment - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240104967 titled 'Synthetic Gaze Enrollment
Simplified Explanation
A personalized eye model is used to generate synthetic gaze features and estimate corresponding synthetic gaze poses using an average eye model. A linear regression is applied to generate a gaze correction function that represents differences between the synthetic gaze of the subject eye and that of the average eye model at the display. The personalized eye model cannot be recovered from the gaze correction function, allowing it to be stored unencrypted and available for use during a cold boot of a device prior to login.
- Personalized eye model used to generate synthetic gaze features
- Linear regression applied to create gaze correction function
- Gaze correction function stored unencrypted for use during cold boot
Potential Applications
This technology could be applied in various fields such as:
- Eye tracking systems
- Virtual reality and augmented reality devices
- Human-computer interaction interfaces
Problems Solved
This technology addresses the following issues:
- Improving accuracy of gaze-based interactions
- Enhancing user experience in eye tracking applications
Benefits
The benefits of this technology include:
- Enhanced personalization in gaze tracking
- Improved performance in eye-related applications
- Increased security by storing unencrypted gaze correction function
Potential Commercial Applications
This technology could be valuable in commercial sectors such as:
- Gaming industry for immersive experiences
- Healthcare for medical diagnostics
- Marketing for consumer behavior analysis
Possible Prior Art
Prior art in eye tracking technology includes:
- Existing gaze correction methods in eye tracking systems
- Previous research on personalized eye models
Unanswered Questions
How does this technology impact user privacy?
This technology does not compromise user privacy as the personalized eye model cannot be recovered from the gaze correction function, ensuring data security.
What are the limitations of using an average eye model in this context?
The use of an average eye model may not fully capture individual variations in eye features, potentially leading to inaccuracies in gaze correction.
Original Abstract Submitted
a personalized eye model is used to generate synthetic gaze features at ground-truth eye poses g. corresponding synthetic gaze poses gare estimated from the synthetic gaze features using an average eye model. a linear regression is applied between gand gto generate a gaze correction function. the gaze correction function represents differences between the synthetic gaze gof the subject eye at the display and that of the average eye model gat the display, but does not contain security- or privacy-sensitive information. further, the personalized eye model cannot be recovered from the gaze correction function, and thus the gaze correction function can be stored unencrypted and available for use during a cold boot of a device prior to login. on a cold boot of the device, the gaze correction function may be accessed and used with an average eye model to improve gaze-based interactions.
