Lightweight CNN Fingerprint Binarization for Resource-Constrained Devices
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Solution Overview
Problem
Conventional biometric fingerprint recognition methods require high computing power and memory resources, making them unsuitable for consumer devices like smartphones, where performance is restrictive.
Innovation Solution
A method using a lightweight Convolutional Neural Network (CNN) for binarizing fingerprint images, which reduces resource requirements and facilitates efficient feature extraction by generating a binary image, enabling effective classification on consumer devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional biometric approaches use traditional feature extraction methods, then classification accuracy is improved, but computing power and memory resources are excessively consumed
Solution Approach 1:
The patent segments the fingerprint image processing into distinct levels: Level 1 extracts general pattern characteristics (ridge flow, orientation), Level 2 extracts minutiae points, and Level 3 extracts detailed features. This segmentation allows the system to process only the necessary level of detail for each authentication scenario, reducing overall computational burden while maintaining accuracy where needed.
Solution Approach 2:
The patent extracts only the essential characteristic information from fingerprint images rather than processing the entire image data. By focusing on specific features (ridge flow matrices, minutiae points, orientation maps) and discarding redundant information, the system achieves accurate classification with significantly reduced computational resources.
2Ease of operation
If fingerprint recognition is implemented on consumer equipment like smartphones, then accessibility and ease of operation are improved, but device complexity and resource constraints increase
Solution Approach 1:
The patent implements a dynamic feature extraction approach where the system can adapt the level of processing based on available resources and authentication requirements. The multi-level framework allows consumer devices to adjust computational depth dynamically, making fingerprint recognition accessible on resource-constrained smartphones while maintaining flexibility for different use cases.
Solution Approach 2:
The system performs self-optimization by automatically selecting appropriate processing levels and feature extraction depths based on the specific authentication context and device capabilities. This eliminates the need for manual configuration and allows consumer devices to efficiently handle fingerprint recognition without requiring specialized hardware adjustments.
3Measurement precision
If detailed feature extraction is performed to improve classification precision, then measurement precision is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary feature extraction at Level 1 (general patterns, ridge flow) before proceeding to more detailed analysis. This preliminary processing quickly filters and organizes the fingerprint data, so that when Level 2 or Level 3 extraction is needed, the system already has pre-processed information ready, significantly reducing the time required for detailed feature extraction while maintaining accuracy.
Data Source
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AI summary
The present invention relates to a method for extracting desired features from a fingerprint represented by an input image, the method being characterized in that it comprises the implementation, by data processing means (21) of a client (2), of steps of: (a) Binarization of said input image by means of a convolutional neural network, CNN, so as to generate a so-called binary image; (b) Processing of said binary image so as to extract said desired features from the fingerprint represented by said input image.