Eye Image Selection for VR Headsets
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Solution Overview
Problem
Current virtual and augmented reality technologies face challenges in providing comfortable, natural-feeling, and rich presentations of virtual image elements amidst real-world imagery due to complexities in human visual perception, particularly in accurately determining eye pose and generating iris codes with high image quality.
Innovation Solution
A method for eye image set selection, collection, and combination using a hardware computer processor in a head-mounted display system, which involves obtaining eye images, determining image quality metrics, and applying image fusion or iris code fusion operations to generate hybrid images and codes, enhancing biometric applications and depth perception in VR, AR, and MR experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple eye images are obtained and processed to improve iris code quality, then biometric identification accuracy is improved, but processing time and computational complexity increase
Solution Approach 1:
The system performs preliminary quality assessment on eye images before full processing. Images failing quality thresholds are rejected early, preventing wasted computational resources on poor-quality data. This preliminary filtering action resolves the contradiction by ensuring only viable images undergo time-consuming iris code generation.
Solution Approach 2:
The patent extracts and processes only the most relevant features from eye images - specifically isolating the iris region and extracting key characteristics for code generation. By taking out only the essential information needed for biometric identification rather than processing entire images, the system maintains high accuracy while reducing computational burden and processing time.
2Reliability
If image quality thresholds are applied to select only high-quality eye images, then iris code reliability is improved, but the quantity of usable images decreases
Solution Approach 1:
The system merges multiple low-quality or partial eye images into a single composite high-quality image. By combining images taken at different times or angles, the system reconstructs a complete, high-resolution iris image that meets quality thresholds. This merging approach maintains reliability while increasing the quantity of usable images from sources that would individually fail quality checks.
Solution Approach 2:
The patent applies partial processing to images that don't fully meet quality standards. Rather than completely discarding subthreshold images, the system processes them to the extent possible, extracting whatever useful iris information is available and combining it with other images. This partial action approach maximizes the quantity of usable data while maintaining overall reliability through selective application of quality thresholds.
Data Source
AI summary
Systems and methods for eye image set selection, eye image collection, and eye image combination are described. Embodiments of the systems and methods for eye image set selection can include comparing a determined image quality metric with an image quality threshold to identify an eye image passing an image quality threshold, and selecting, from a plurality of eye images, a set of eye images that passes the image quality threshold.