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Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

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Published on: November 23, 2019

Fingerprint authentication based on deep convolutional descent inversion tomography.

Shuainan Chen¹, Chengwei Zhao¹, Jiahao Ren¹

¹State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China.

|June 1, 2024

Summary

This summary is machine-generated.

This study introduces a novel fingerprint authentication system using Lamb waves and deep learning. The method enhances accuracy and speed, overcoming challenges with wet fingers and false minutiae detection.

Keywords:

Deep fast inversion tomography Fingerprint authentication Fingerprint inversion Lamb waves Mask R-CNN

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Area of Science:

Biometrics
Signal Processing
Machine Learning

Background:

Traditional fingerprint authentication struggles with wet fingers and false minutiae.
Existing methods require efficient feature extraction and matching for reliable identification.

Purpose of the Study:

To develop a fast fingerprint inversion and authentication method using Lamb waves and deep learning.
To improve accuracy and robustness in fingerprint recognition, especially for challenging conditions.

Main Methods:

Integration of deep learning with multi-scale fusion for fingerprint analysis.
Utilizing deep fast inversion tomography (DeepFIT) for accelerated ultrasonic array reconstruction and minutia inversion.
Employing Mask R-CNN for segmentation and matching of multi-scale fingerprint features.

Main Results:

Achieved sub-millimeter-level fingerprint minutia inversion with suppressed artifacts.
Demonstrated improved accuracy and reliability in authentication by extracting meaningful minutia.
Validated high accuracy, robustness, and speed in the proposed fingerprint authentication process.

Conclusions:

The developed method optimizes fingerprint authentication by addressing limitations of existing techniques.
Deep learning integration significantly enhances the performance of fingerprint recognition systems.
The approach offers a promising solution for secure and efficient biometric identification.