{"title":"FV-DDC: A novel finger-vein recognition model with deformation detection and correction","authors":"","doi":"10.1016/j.bspc.2024.107098","DOIUrl":null,"url":null,"abstract":"<div><div>Finger vein recognition has gained widespread attention for personal identification due to its robustness and resistance to forgery. While Convolutional Neural Network (CNN)-based finger vein recognition algorithms have shown promising performance, several challenges remain. Firstly, existing methods often fail to effectively handle complex finger deformations, such as bending and rotation, which frequently occur in real-world applications. Secondly, CNN-based approaches typically require large training datasets, yet the available finger vein datasets are limited in size. To address these challenges, this paper presents a novel CNN-based finger vein recognition algorithm, FV-DDC, incorporating a lightweight finger deformation correction module, FVTN. The FVTN module autonomously learns and corrects finger deformations using matrix transformations, offering a new approach to CNN-based deformation correction. The primary advantages of FV-DDC are twofold: automatic finger deformation correction, which simplifies preprocessing, and data augmentation during deformation correction, reducing the dependency on large datasets. Extensive experiments were conducted on three publicly available datasets to validate the effectiveness of the proposed algorithm. The results show that FV-DDC achieves superior recognition performance, particularly in scenarios involving missing data and deformation interference, with recognition accuracies of 99.62% on HKPU, 99.80% on FV-USM, and 98.74% on SDUMLA.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical Signal Processing and Control","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S174680942401156X","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Finger vein recognition has gained widespread attention for personal identification due to its robustness and resistance to forgery. While Convolutional Neural Network (CNN)-based finger vein recognition algorithms have shown promising performance, several challenges remain. Firstly, existing methods often fail to effectively handle complex finger deformations, such as bending and rotation, which frequently occur in real-world applications. Secondly, CNN-based approaches typically require large training datasets, yet the available finger vein datasets are limited in size. To address these challenges, this paper presents a novel CNN-based finger vein recognition algorithm, FV-DDC, incorporating a lightweight finger deformation correction module, FVTN. The FVTN module autonomously learns and corrects finger deformations using matrix transformations, offering a new approach to CNN-based deformation correction. The primary advantages of FV-DDC are twofold: automatic finger deformation correction, which simplifies preprocessing, and data augmentation during deformation correction, reducing the dependency on large datasets. Extensive experiments were conducted on three publicly available datasets to validate the effectiveness of the proposed algorithm. The results show that FV-DDC achieves superior recognition performance, particularly in scenarios involving missing data and deformation interference, with recognition accuracies of 99.62% on HKPU, 99.80% on FV-USM, and 98.74% on SDUMLA.
期刊介绍:
Biomedical Signal Processing and Control aims to provide a cross-disciplinary international forum for the interchange of information on research in the measurement and analysis of signals and images in clinical medicine and the biological sciences. Emphasis is placed on contributions dealing with the practical, applications-led research on the use of methods and devices in clinical diagnosis, patient monitoring and management.
Biomedical Signal Processing and Control reflects the main areas in which these methods are being used and developed at the interface of both engineering and clinical science. The scope of the journal is defined to include relevant review papers, technical notes, short communications and letters. Tutorial papers and special issues will also be published.