Iris Limbic Boundary Detection via Corneal Curvature
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Solution Overview
Problem
Conventional methods for identifying the limbic boundary of the iris in eye imagery face challenges due to its soft and poorly defined nature, often leading to inaccuracies and precision issues, especially under infrared lighting, where it can be mistaken for other eye features like eyelids or shadows.
Innovation Solution
The use of 3-D geometry of the eye, specifically the intersection of the corneal bulge with the eye surface, to accurately identify the limbic boundary, leveraging inward-facing eye imaging cameras in wearable head-mounted displays to capture high-resolution eye imagery and extract unique iris features for biometric identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to identify the limbic boundary in eye imagery, then the process is simple, but the accuracy and precision are poor due to the soft and poorly defined nature of the boundary
Solution Approach 1:
The patent introduces corneal curvature as an intermediary geometric feature to indirectly identify the limbic boundary. Instead of directly detecting the soft limbic boundary, the system uses the corneal curvature - a more stable and detectable geometric property - as a mediator to infer the boundary location, thereby improving accuracy without significantly increasing method complexity
Solution Approach 2:
The patent replaces conventional image processing techniques (which directly analyze the soft limbic boundary) with a geometric approach based on corneal curvature measurement. This substitution of the detection mechanism from direct boundary analysis to geometric feature inference improves measurement precision while maintaining reasonable system complexity
2Reliability
If infrared lighting is used for eye imaging, then the biometric information can be captured, but the limbic boundary becomes harder to distinguish from other eye features like eyelids or shadows
Solution Approach 1:
The patent applies local quality analysis by focusing on the specific geometric properties of the cornea at the limbal region. By analyzing the local curvature characteristics of the cornea in the periocular region, the system can distinguish the limbic boundary from other eye features, improving detection accuracy under infrared lighting conditions
Solution Approach 2:
The patent leverages the spherical curvature of the cornea as a key distinguishing feature. The corneal curvature provides a unique geometric signature that differs from the geometry of eyelids and shadows, enabling reliable detection of the limbic boundary even under infrared illumination where color and texture information are limited
Data Source
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AI summary
Examples of systems and methods for determining a limbic boundary of an iris of an eye are provided. Properties of the corneal bulge can be computed from eye images. An intersection of the corneal bulge with the eye surface (e.g., sclera) can be determined as the limbic boundary. The determined limbic boundary can be used for iris segmentation or biometric applications. A head mounted display can include a camera that images the eye and a processor that analyzes the eye images and determines the limbic boundary.