PoreNet: CNN-Based Pore Descriptor for High-Resolution Fingerprint Recognition

With the development of high-resolution fingerprint scanners, high-resolution fingerprint-based biometric recognition has received increasing attention in recent years. This paper presents a pore feature-based approach for biometric recognition. Our approach employs a convolutional neural network (C...

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Bibliographic Details
Published in:IEEE sensors journal Vol. 20; no. 16; pp. 9305 - 9313
Main Authors: Anand, Vijay, Kanhangad, Vivek
Format: Journal Article
Language:English
Published: New York IEEE 15.08.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Artificial neural networks
Biometric recognition systems
Biometrics
Computational modeling
convolutional neural network
Convolutional neural networks
cross-sensor fingerprints
Datasets
Euclidean geometry
Feature extraction
fingerprint recognition
Fingerprint verification
Fingerprinting
High resolution
High-resolution fingerprints
Image detection
Image recognition
Learning
Object recognition
pore descriptor
Scanners
Sensor phenomena and characterization
Training
ISSN:1530-437X, 1558-1748
Online Access:Get full text
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Summary:With the development of high-resolution fingerprint scanners, high-resolution fingerprint-based biometric recognition has received increasing attention in recent years. This paper presents a pore feature-based approach for biometric recognition. Our approach employs a convolutional neural network (CNN) model, DeepResPore, to detect pores in the input fingerprint image. Thereafter, a CNN-based descriptor is computed for a patch around each detected pore. Specifically, we have designed a residual learning-based CNN, referred to as PoreNet that learns distinctive feature representation from pore patches. For verification, a matching score is generated by comparing the pore descriptors, obtained from a pair of fingerprint images, in a bi-directional manner using the Euclidean distance. The proposed approach for high-resolution fingerprint recognition achieves 2.27% and 0.24% equal error rates (EERs) on partial (DBI) and complete (DBII) fingerprints of the benchmark PolyU HRF dataset. Most importantly, it achieves lower FMR1000 and FMR10000 values than the current state-of-the-art approach on both the datasets. Further, this is the first study to report the performance of a learning-based fingerprint recognition approach on cross-sensor fingerprint images.
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2020.2987287