Biometric Image Processing via Pixel Sub-Image Segmentation
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Solution Overview
Problem
Biometric image processing requires significant computing resources and memory, which can be a challenge for electronic devices with limited resources, hindering effective biometric recognition functions.
Innovation Solution
The method involves generating a sub-image by capturing grayscale values of a portion of pixels from the original biometric image, performing image processing on the sub-image, and then replacing the corresponding pixel values in the original image, along with a mask operation on unreplaced pixels to optimize the image, thereby reducing computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If regular biometric image processing is performed on the entire biometric image, then image optimization quality is improved, but computing resource consumption increases
Solution Approach 1:
The biometric image is divided into multiple regions of interest (ROIs) based on pixel grayscale value distributions. Instead of processing the entire image uniformly, the method segments the image into distinct regions and applies targeted processing only to necessary areas, thereby reducing overall computing resource consumption while maintaining image optimization quality in critical regions.
Solution Approach 2:
Different processing strategies are applied to different regions of the biometric image based on their specific characteristics. Regions with significant grayscale variations receive enhanced processing, while uniform regions receive minimal or no processing. This local quality approach ensures optimal image quality where needed while conserving computing resources in less critical areas.
2Measurement precision
If comprehensive image processing procedures are applied to the biometric image, then recognition accuracy is improved, but memory space requirements increase
Solution Approach 1:
The image processing is segmented into multiple stages and regions. Intermediate processing results are stored only for current regions being processed rather than the entire image, significantly reducing memory space requirements. The segmentation allows the system to maintain high recognition accuracy through comprehensive processing while using minimal memory at any given time.
Solution Approach 2:
The method applies processing procedures selectively to regions that contribute most to recognition accuracy, rather than uniformly processing the entire image. By identifying and processing only the most relevant regions (those with significant grayscale variations and biometric features), the system achieves high recognition accuracy with reduced memory consumption compared to comprehensive full-image processing.
Data Source
AI summary
A biometric image processing method and an electronic device are provided. The biometric image processing method includes the following steps: obtaining a first biometric image; capturing a plurality of grayscale values of a first portion of pixels of the first biometric image and combining the plurality of grayscale values of the first portion of pixels of the first biometric image to generate a first sub-image; performing an image processing procedure on the first sub-image; replacing the plurality of grayscale values of the first portion of pixels of the first biometric image by a plurality of grayscale values of all of the pixels of the first sub-image after the image processing; and performing a mask operation on a plurality of unreplaced grayscale values of other portions of pixels of the first biometric image to generate a second biometric image.