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Toward Efficient Palmprint Feature Extraction by Learning a Single-Layer Convolution Network
Fei, Lunke1; Zhao, Shuping1; Jia, Wei2; Zhang, Bob3; Wen, Jie4; Xu, Yong4
2022-03
Source PublicationIEEE Transactions on Neural Networks and Learning Systems
ISSN2162-237X
Abstract

In this article, we propose a collaborative palmprint-specific binary feature learning method and a compact network consisting of a single convolution layer for efficient palmprint feature extraction. Unlike most existing palmprint feature learning methods, such as deep-learning, which usually ignore the inherent characteristics of palmprints and learn features from raw pixels of a massive number of labeled samples, palmprint-specific information, such as the direction and edge of patterns, is characterized by forming two kinds of ordinal measure vectors (OMVs). Then, collaborative binary feature codes are jointly learned by projecting double OMVs into complementary feature spaces in an unsupervised manner. Furthermore, the elements of feature projection functions are integrated into OMV extraction filters to obtain a collection of cascaded convolution templates that form a single-layer convolution network (SLCN) to efficiently obtain the binary feature codes of a new palmprint image within a single-stage convolution operation. Particularly, our proposed method can easily be extended to a general version that can efficiently perform feature extraction with more than two types of OMVs. Experimental results on five benchmark databases show that our proposed method achieves very promising feature extraction efficiency for palmprint recognition.

KeywordBiometrics Codes Collaboration Compact Convolution Network Convolution Feature Extraction Joint Feature Learning Palmprint Recognition Palmprint Recognition. Representation Learning Training
DOI10.1109/TNNLS.2022.3160597
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000777137500001
Scopus ID2-s2.0-85127467425
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorWen, Jie
Affiliation1.School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, China.
2.School of Computer and Information, Hefei University of Technology, Hefei 230009, China.
3.Department of Computer and Information Science, University of Macau, Taipa, Macau.
4.School of Computer Science and Technology, Harbin Institute of Technology at Shenzhen, Shenzhen 518055, China.
Recommended Citation
GB/T 7714
Fei, Lunke,Zhao, Shuping,Jia, Wei,et al. Toward Efficient Palmprint Feature Extraction by Learning a Single-Layer Convolution Network[J]. IEEE Transactions on Neural Networks and Learning Systems,2022.
APA Fei, Lunke,Zhao, Shuping,Jia, Wei,Zhang, Bob,Wen, Jie,&Xu, Yong.(2022).Toward Efficient Palmprint Feature Extraction by Learning a Single-Layer Convolution Network.IEEE Transactions on Neural Networks and Learning Systems.
MLA Fei, Lunke,et al."Toward Efficient Palmprint Feature Extraction by Learning a Single-Layer Convolution Network".IEEE Transactions on Neural Networks and Learning Systems (2022).
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