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A Generative Method for Finger Knuckle Print Recognition
Wang, Yuqi1; Zhang, Bob1; Li, Shuyi2; Yang, Hao1
2025
Source PublicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume15328 LNCS
Pages288-302
AbstractFinger knuckle print (FKP), known as a biological feature, has drawn great research attention in the field of biometrics recognition. That being said, the development of finger knuckle print recognition is still limited by the lack of data and the difficulties in the extraction of its region of interest (ROI). To resolve these issues, this paper proposes a generative method based on the simulation of the curve distribution of a finger knuckle print to generate reasonable masks of finger knuckle points. Following this, generative adversarial networks (GANs) are applied with the masks to generate the pseudo finger knuckle point images. This method can provide large amounts of training data for recognition as well as directly supplying the region of interest. Experimental results show that the generated finger knuckle print examples can effectively augment the training data for the recognition model.
KeywordBiometrics Recognition Finger Knuckle Print Generative Method
DOI10.1007/978-3-031-78104-9_20
URLView the original
Language英語English
Scopus ID2-s2.0-85211792355
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Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.PAMI Research Group, Department of Computer and Information Science, University of Macau, Macau SAR, China
2.Department of Information, Beijing University of Technology, Beijing, 100124, China
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Wang, Yuqi,Zhang, Bob,Li, Shuyi,et al. A Generative Method for Finger Knuckle Print Recognition[C], 2025, 288-302.
APA Wang, Yuqi., Zhang, Bob., Li, Shuyi., & Yang, Hao (2025). A Generative Method for Finger Knuckle Print Recognition. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 15328 LNCS, 288-302.
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