Iris Region Extraction Using Neural Network Classification Maps
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Solution Overview
Problem
Current iris recognition technologies face challenges in accurately and efficiently extracting the iris region from images, particularly in degraded or noisy conditions, which affects the overall accuracy and speed of the authentication process.
Innovation Solution
The method employs a trained neural network model with convolutional hidden layers, using backpropagation for training, to extract the iris region by estimating geometric parameters such as circles, ellipses, and curves, and generates a classification map to refine the segmentation, improving accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional iris segmentation methods are used, then the extraction process can be completed, but the accuracy is reduced in degraded or noisy image conditions
Solution Approach 1:
The patent divides the iris extraction task into multiple stages: initial segmentation to identify candidate regions, followed by refinement stages that progressively improve boundary accuracy. This multi-stage segmentation approach allows the system to handle degraded images by breaking down the complex extraction task into manageable steps, each addressing specific challenges in noisy conditions.
Solution Approach 2:
The patent applies preliminary processing steps including noise filtering, contrast enhancement, and edge detection before the main segmentation process. These preliminary actions prepare the degraded image data by reducing noise and enhancing relevant features, making the subsequent segmentation more accurate despite poor image quality.
2Measurement precision
If complex segmentation algorithms are applied to improve accuracy, then extraction precision increases, but computational time and resource requirements increase
Solution Approach 1:
The patent segments the computation into hierarchical stages: a fast initial segmentation pass that provides coarse accuracy, followed by targeted refinement only in critical regions. This avoids applying computationally intensive algorithms to the entire image, reducing overall processing time while maintaining high accuracy where needed.
Solution Approach 2:
The patent applies different levels of processing complexity to different regions of the image. High-accuracy computationally intensive methods are applied only to critical boundary regions, while less complex methods are used in stable regions. This local differentiation maintains overall accuracy while significantly reducing total computational time.
3Measurement precision
If multiple processing stages are used to handle degraded images, then extraction accuracy improves, but device complexity increases
Solution Approach 1:
The patent combines multiple processing functions into integrated modules. For example, noise reduction and edge detection are merged into a single preprocessing stage, and segmentation and boundary refinement are combined in unified algorithms. This merging reduces the number of separate components while maintaining the multi-stage processing benefits for handling degraded images.
Data Source
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AI summary
A method and apparatus for extracting an iris region is disclosed. The apparatus may generate a classification map associated with an iris region from an eye image using a trained neural network model, estimate a geometric parameter associated with the iris region using the generated classification map, and extract the iris region from the eye image based on the estimated geometric parameter.