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PoreNet: CNN-Based Pore Descriptor for High-Resolution Fingerprint Recognition.
- Source :
- IEEE Sensors Journal; 8/15/2020, Vol. 20 Issue 16, p9305-9313, 9p
- Publication Year :
- 2020
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 1530437X
- Volume :
- 20
- Issue :
- 16
- Database :
- Complementary Index
- Journal :
- IEEE Sensors Journal
- Publication Type :
- Academic Journal
- Accession number :
- 144714665
- Full Text :
- https://doi.org/10.1109/JSEN.2020.2987287