Iris Recognition Image Selection Using Pupil Radius and Contrast
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Solution Overview
Problem
Iris recognition in mobile devices is hindered by environmental conditions such as lighting variations, occlusions, and hardware limitations, leading to degraded image quality, recognition errors, and increased resource consumption.
Innovation Solution
A method and apparatus that perform multi-staged checks on acquired images based on pupil information, such as radius and contrast, to select suitable images for iris recognition, thereby improving image quality and reducing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If iris recognition is performed on all acquired images, then recognition coverage is improved, but resource consumption and processing time increase
Solution Approach 1:
The patent applies preliminary quality assessment actions before iris recognition processing. The processor evaluates pupil information (radius, contrast, position) and image quality metrics (illumination, occlusion, sharpness) to pre-screen images. Only images meeting quality thresholds proceed to iris recognition, eliminating wasted processing on poor-quality images and reducing power consumption while maintaining recognition accuracy.
2Measurement precision
If multi-staged quality checks are performed on images, then recognition accuracy is improved, but processing time increases
Solution Approach 1:
The patent segments the quality assessment process into multiple independent stages: pupil detection, pupil information extraction (radius, contrast, position), illumination assessment, occlusion detection, and sharpness evaluation. Each stage processes specific features independently and filters images progressively. This segmentation allows parallel processing of different quality metrics and enables early termination of poor-quality images at any stage, reducing overall processing time while maintaining comprehensive quality assessment.
3Productivity
If pupil information-based filtering is applied, then processing efficiency is improved, but image selection accuracy may deteriorate
Solution Approach 1:
The patent merges multiple pupil information parameters (pupil radius, pupil contrast, pupil position) with additional image quality metrics (illumination quality, occlusion level, sharpness) into a comprehensive image selection criterion. Instead of relying on pupil information alone, the system combines these diverse metrics with weighted scoring to evaluate overall image suitability. This merging ensures that images selected for iris recognition meet multiple quality dimensions, preventing selection errors while maintaining high processing efficiency through the unified assessment framework.
Data Source
AI summary
An apparatus for recognizing an iris is provided. The apparatus includes an image acquisition module configured to acquire a plurality of images, and a processor configured to select at least one image for iris recognition from among the plurality of images based on pupil information of each of the plurality of images, and recognize an iris in at least one image, wherein the pupil information includes at least one of information about a pupil radius and information about a pupil contrast.