A novel fingerprint classification method based on deep learning

Wang, R; Han, C; Guo, T

A novel fingerprint classification method based on deep learning

Wang, R Han, C Guo, T

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Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Pattern Recognition, 2016, 0 pp. 931 - 936
Issue Date:: 2016-01-01

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Field	Value	Language
dc.contributor.author	Wang, R	en_US
dc.contributor.author	Han, C	en_US
dc.contributor.author	Guo, T	en_US
dc.date.issued	2016-01-01	en_US
dc.identifier.citation	Proceedings - International Conference on Pattern Recognition, 2016, 0 pp. 931 - 936	en_US
dc.identifier.isbn	9781509048472	en_US
dc.identifier.issn	1051-4651	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126101
dc.description.abstract	© 2016 IEEE. Fingerprint classification is an effective technique for reducing the candidate numbers of fingerprints in the stage of matching in automatic fingerprint identification system (AFIS). In recent years, deep learning is an emerging technology which has achieved great success in many fields, such as image processing, computer vision. In this paper, we have a preliminary attempt on the traditional fingerprint classification problem based on the new depth neural network method. For the four-class problem, only choosing orientation field as the classification feature, we achieve 91.4% accuracy using the stacked sparse autoencoders (SAE) with three hidden layers in the NIST-DB4 database. And then two classification probabilities are used for fuzzy classification which can effectively enhance the accuracy of classification. By only adjusting the probability threshold, we get the accuracy of classification is 96.1% (setting threshold is 0.85), 97.2% (setting threshold is 0.90) and 98.0% (setting threshold is 0.95) with a single layer SAE. Applying the fuzzy method, we obtain higher accuracy.	en_US
dc.relation.ispartof	Proceedings - International Conference on Pattern Recognition	en_US
dc.relation.isbasedon	10.1109/ICPR.2016.7899755	en_US
dc.title	A novel fingerprint classification method based on deep learning	en_US
dc.type	Conference Proceeding
utslib.citation.volume	0	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	0	en_US

Abstract:

© 2016 IEEE. Fingerprint classification is an effective technique for reducing the candidate numbers of fingerprints in the stage of matching in automatic fingerprint identification system (AFIS). In recent years, deep learning is an emerging technology which has achieved great success in many fields, such as image processing, computer vision. In this paper, we have a preliminary attempt on the traditional fingerprint classification problem based on the new depth neural network method. For the four-class problem, only choosing orientation field as the classification feature, we achieve 91.4% accuracy using the stacked sparse autoencoders (SAE) with three hidden layers in the NIST-DB4 database. And then two classification probabilities are used for fuzzy classification which can effectively enhance the accuracy of classification. By only adjusting the probability threshold, we get the accuracy of classification is 96.1% (setting threshold is 0.85), 97.2% (setting threshold is 0.90) and 98.0% (setting threshold is 0.95) with a single layer SAE. Applying the fuzzy method, we obtain higher accuracy.

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http://hdl.handle.net/10453/126101