Invariant Radial Iris Segmentation for Biometric Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing iris recognition systems face challenges in accurately segmenting the iris under uncontrolled conditions, leading to errors in biometric data acquisition and validation, especially when subjects are scanned at a distance or under varying environmental conditions.
Innovation Solution
A simplified polar segmentation (POSE) technique that focuses on identifying peaks and valleys in the iris, eliminating the need for accurate segmentation of the outer iris boundary, and uses a new encoding scheme that relies on the magnitude of detected peaks relative to a referenced peak, reducing the computational load and eliminating the need for normalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If circular/elliptical contour segmentation is used to maximize segmentation accuracy, then segmentation precision is improved, but device complexity and computational load increase
Solution Approach 1:
The patent extracts only the essential feature (iris center location) needed for segmentation, eliminating the need for complex circular/elliptical contour detection. By focusing on detecting the iris center point through correlation techniques rather than segmenting the entire iris boundary, the system achieves accurate segmentation with significantly reduced computational complexity.
2Measurement precision
If constraints are placed on lighting levels, position, and temperature to improve segmentation accuracy, then segmentation precision is improved, but ease of operation deteriorates
Solution Approach 1:
The system performs self-adjustment by automatically detecting the iris center and adapting the segmentation process to the actual iris position and characteristics. The correlation-based detection method inherently compensates for variations in lighting, position, and environmental conditions without requiring external constraints or manual adjustments, enabling operation in uncontrolled environments.
3Reliability
If normalization process is implemented to improve recognition accuracy, then reliability is improved, but productivity decreases
Solution Approach 1:
The patent performs preliminary normalization by detecting the iris center and establishing the correct segmentation geometry before feature extraction begins. This preliminary positioning ensures that subsequent feature extraction operates on properly aligned data, achieving accurate recognition without requiring time-consuming normalization steps during the main processing pipeline.
Data Source
AI summary
A method and computer product are presented for identifying a subject by biometric analysis of an eye. First, an image of the iris of a subject to be identified is acquired. Texture enhancements may be done to the image as desired, but are not necessary. Next, the iris image is radially segmented into a selected number of radial segments, for example 200 segments, each segment representing 1.8° of the iris scan. After segmenting, each radial segment is analyzed, and the peaks and valleys of color intensity are detected in the iris radial segment. These detected peaks and valleys are mathematically transformed into a data set used to construct a template. The template represents the subject's scanned and analyzed iris, being constructed of each transformed data set from each of the radial segments. After construction, this template may be stored in a database, or used for matching purposes if the subject is already registered in the database.