Neural Network Fingerprint Reconstruction from Partial Images
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Solution Overview
Problem
Current fingerprint recognition methods face inefficiencies in processing partial fingerprint images, leading to lower recognition performance and user experience, especially when handling multiple images simultaneously.
Innovation Solution
A method utilizing a neural network to rebuild entire fingerprint images from partial images by training the network with partial texture images, outputting feature values, and comparing them to stored values in a database to determine similarity and authenticate users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex algorithms are used for denoising, supplementation, interpolation and stitching of partial fingerprint images, then the entire fingerprint image can be restored, but the execution performance is reduced
Solution Approach 1:
The patent replaces traditional mechanical image processing algorithms (denoising, supplementation, interpolation, stitching) with a neural network-based system. The neural network is trained to directly reconstruct entire fingerprint images from partial images, substituting the complex sequential algorithmic process with a learned mapping function that achieves similar restoration quality with improved execution performance.
2Quantity of substance
If multiple partial fingerprint images are processed simultaneously using multi-threading, then the processing capacity is increased, but the execution performance decreases
Solution Approach 1:
The patent merges multiple partial fingerprint image processing operations into a single unified neural network inference process. Instead of processing multiple images through separate algorithmic pipelines with threading overhead, the neural network accepts multiple partial images as input and produces reconstructed entire images in a consolidated operation, reducing system overhead and improving overall execution performance.
Data Source
AI summary
A method, storage media and neural network for rebuilding biometric feature is provided. The method includes inputting the partial texture image obtained to the neural network and outputting a predictive value of an entire texture image that is output by the neural network. The above technical solution is via the neural network used to process images and the neural network includes the feature value layer. A plurality of the partial texture images is converted to the feature values at the technical level, and the composite calculation of a plurality of partial texture images is avoided on the application level. Because the entire texture image is not synthesized in the end, data leakage and theft are avoided. Thus, the security of the method for analyzing texture image is improved.