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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| Vydáno v: | IEEE sensors journal Ročník 20; číslo 16; s. 9305 - 9313 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
IEEE
15.08.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1530-437X, 1558-1748 |
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| 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. |
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| AbstractList | 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. |
| Author | Kanhangad, Vivek Anand, Vijay |
| Author_xml | – sequence: 1 givenname: Vijay orcidid: 0000-0003-1544-4694 surname: Anand fullname: Anand, Vijay email: phd1401202011@iiti.ac.in organization: Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore, India – sequence: 2 givenname: Vivek orcidid: 0000-0002-9791-3695 surname: Kanhangad fullname: Kanhangad, Vivek email: kvivek@iiti.ac.in organization: Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore, India |
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| Cites_doi | 10.1016/j.patcog.2014.04.008 10.1016/j.patcog.2010.02.016 10.1109/ISBA.2017.7947685 10.1109/CVPRW.2015.7301328 10.1109/CVPR.2015.7298682 10.1006/cviu.1999.0832 10.1109/CVPR.2016.90 10.1007/978-3-540-25976-3_12 10.1016/j.patcog.2011.02.010 10.1109/ICCV.2015.22 10.1145/358669.358692 10.1016/j.patcog.2009.08.004 10.1007/978-1-84882-254-2 10.1109/TCSVT.2018.2875147 10.1109/ICPR.2014.299 10.1007/s10044-019-00805-3 10.1109/ICPR.2010.403 10.1023/B:VISI.0000029664.99615.94 10.1109/ICPR.2008.4761304 10.1109/CVPR.2008.4587673 10.5244/C.29.41 10.1109/ICPR.2008.4761458 10.1007/978-3-642-01793-3_61 10.1109/5.628710 10.1117/1.JEI.28.2.020502 10.1109/TPAMI.2007.250596 10.1109/TIM.2010.2062610 10.1109/ICIP.2019.8803128 |
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| References | ref13 ref12 torr (ref31) 2000; 78 ref15 ref14 ref33 ref11 ref10 ref2 ref1 ref39 ref17 ref38 ref16 ref18 abadi (ref36) 2016 meagher (ref3) 2005 kryszczuk (ref6) 2004 kingma (ref35) 2014; abs 1412 6980 ref24 ref23 (ref34) 2009 ref26 ref25 dahia (ref19) 2018 ref22 hartley (ref30) 2003 ref21 cappelli (ref37) 2011 yaroslav (ref40) 2015 ref28 ref27 ref29 ref8 ref7 ioffe (ref32) 2015; abs 1502 3167 mishchuk (ref20) 2017 ref5 stosz (ref4) 1994; 2277 zhao (ref9) 2009 |
| References_xml | – ident: ref39 doi: 10.1016/j.patcog.2014.04.008 – ident: ref10 doi: 10.1016/j.patcog.2010.02.016 – ident: ref16 doi: 10.1109/ISBA.2017.7947685 – ident: ref15 doi: 10.1109/CVPRW.2015.7301328 – ident: ref23 doi: 10.1109/CVPR.2015.7298682 – volume: 78 start-page: 138 year: 2000 ident: ref31 article-title: MLESAC: A new robust estimator with application to estimating image geometry publication-title: Comput Vis Image Understand doi: 10.1006/cviu.1999.0832 – ident: ref27 doi: 10.1109/CVPR.2016.90 – ident: ref7 doi: 10.1007/978-3-540-25976-3_12 – start-page: 1 year: 2011 ident: ref37 article-title: Fingerprint verification competition at IJCB 2011 publication-title: Proc Int Joint Conf Biometrics (IJCB 2011) – ident: ref13 doi: 10.1016/j.patcog.2011.02.010 – start-page: 83 year: 2004 ident: ref6 article-title: Extraction of level 2 and level 3 features for fragmentary fingerprint publication-title: Proc 2nd COST Action 275 Workshop – start-page: 265 year: 2016 ident: ref36 article-title: Tensorflow: A system for large-scale machine learning publication-title: Proc of USENIX Symp on Operating Systems Design and Implementation (OSDI) – ident: ref25 doi: 10.1109/ICCV.2015.22 – ident: ref38 doi: 10.1145/358669.358692 – volume: abs 1502 3167 start-page: 1 year: 2015 ident: ref32 article-title: Batch normalization: Accelerating deep network training by reducing internal covariate shift publication-title: CoRR – volume: 2277 start-page: 2277 year: 1994 ident: ref4 article-title: Automated system for fingerprint authentication using pores and ridge structure publication-title: Proc SPIE – ident: ref11 doi: 10.1016/j.patcog.2009.08.004 – year: 2018 ident: ref19 article-title: Automatic dataset annotation to learn CNN pore description for fingerprint recognition publication-title: arXiv 1809 10229 – ident: ref1 doi: 10.1007/978-1-84882-254-2 – ident: ref21 doi: 10.1109/TCSVT.2018.2875147 – ident: ref14 doi: 10.1109/ICPR.2014.299 – ident: ref29 doi: 10.1007/s10044-019-00805-3 – year: 2009 ident: ref34 – ident: ref12 doi: 10.1109/ICPR.2010.403 – ident: ref33 doi: 10.1023/B:VISI.0000029664.99615.94 – volume: abs 1412 6980 start-page: 1 year: 2014 ident: ref35 article-title: Adam: A method for stochastic optimization publication-title: CoRR – ident: ref18 doi: 10.1109/ICPR.2008.4761304 – ident: ref28 doi: 10.1109/CVPR.2008.4587673 – start-page: 4826 year: 2017 ident: ref20 article-title: Working hard to know your neighbor's margins: Local descriptor learning loss publication-title: Proc NIPS – start-page: 1180 year: 2015 ident: ref40 article-title: Unsupervised domain adaptation by backpropagation publication-title: Proc ICML – ident: ref24 doi: 10.5244/C.29.41 – ident: ref17 doi: 10.1109/ICPR.2008.4761458 – start-page: 597 year: 2009 ident: ref9 article-title: Direct pore matching for fingerprint recognition publication-title: Advances in Biometrics doi: 10.1007/978-3-642-01793-3_61 – start-page: 1 year: 2005 ident: ref3 article-title: Extended fingerprint feature set publication-title: Proc ANSI/NIST ITL 1-2000 Standard Update Workshop – ident: ref5 doi: 10.1109/5.628710 – ident: ref26 doi: 10.1117/1.JEI.28.2.020502 – year: 2003 ident: ref30 publication-title: Multiple View Geometry in Computer Vision – ident: ref8 doi: 10.1109/TPAMI.2007.250596 – ident: ref2 doi: 10.1109/TIM.2010.2062610 – ident: ref22 doi: 10.1109/ICIP.2019.8803128 |
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| SubjectTerms | 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 |
| Title | PoreNet: CNN-Based Pore Descriptor for High-Resolution Fingerprint Recognition |
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