FV-DDC: A novel finger-vein recognition model with deformation detection and correction

IF 4.9 2区 医学 Q1 ENGINEERING, BIOMEDICAL
Hengyi Ren , Lijuan Sun , Jinting Ren , Ying Cao
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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.
FV-DDC:带有形变检测和校正功能的新型指静脉识别模型
手指静脉识别因其鲁棒性和抗伪造性而在个人身份识别领域受到广泛关注。虽然基于卷积神经网络(CNN)的手指静脉识别算法已显示出良好的性能,但仍存在一些挑战。首先,现有方法往往无法有效处理复杂的手指变形,如弯曲和旋转,而这在实际应用中经常出现。其次,基于 CNN 的方法通常需要大量的训练数据集,但现有的手指静脉数据集规模有限。为了应对这些挑战,本文提出了一种基于 CNN 的新型指静脉识别算法 FV-DDC,其中包含一个轻量级手指变形校正模块 FVTN。FVTN 模块利用矩阵变换自主学习和修正手指变形,为基于 CNN 的变形修正提供了一种新方法。FV-DDC 的主要优势有两个方面:自动手指形变校正简化了预处理;形变校正过程中的数据增强减少了对大型数据集的依赖。为了验证所提算法的有效性,我们在三个公开的数据集上进行了广泛的实验。结果表明,FV-DDC 实现了卓越的识别性能,尤其是在涉及数据缺失和形变干扰的情况下,在 HKPU 上的识别准确率为 99.62%,在 FV-USM 上为 99.80%,在 SDUMLA 上为 98.74%。
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来源期刊
Biomedical Signal Processing and Control
Biomedical Signal Processing and Control 工程技术-工程:生物医学
CiteScore
9.80
自引率
13.70%
发文量
822
审稿时长
4 months
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.
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