FVT: Finger Vein Transformer for Authentication
FVT: Finger Vein Transformer for Authentication
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DOI：
10.1109/tim.2022.3173276
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发表时间：
2022-01-01
影响因子：
5.6
通讯作者：
Kang, Wenxiong
中科院分区：
文献类型：
--
作者：
Huang, Junduan;Luo, Weijian;Kang, Wenxiong
近年来，基于深度学习的手指静脉(FV)认证引起了生物识别研究人员的关注，并取得了突破性的成果。以前，卷积神经网络(CNN)是最常用的基于深度学习的FV身份验证方法。近年来，基于视觉转换器(VIT)的方法因其在许多计算机视觉任务中的优异表现而开始受到研究界的关注。在本文中，我们深入研究了VITS，并提出了一种新的FV身份验证模型--FV Transformer(FVT)。FVT由四个关键模块组成：1)条件位置嵌入，它能够根据输入的FV标记动态地生成位置代码；2)加权共享扩展多层感知器(EMLP)，它有助于提取更丰富和更健壮的标记信息；3)局部信息增强型前馈网络(FFN)，它增强了局部信息提取的能力；4)无扩展机制(ELM)，它实现了金字塔结构，从而在Transformer体系结构中引入了多级特征提取能力。为了充分验证FVT的性能和泛化能力，在9个公开可用的FV数据集上进行了实验。烧蚀实验验证了FVT各关键模块的有效性。此外，对比实验表明，FVT的性能优于几种基线变压器模型，并且与最先进的(SOTA)FV身份验证方法相比，获得了与之相当的性能。
In recent years, deep learning-based finger vein (FV) authentication has attracted the attention of biometric researchers and achieved breakthrough results. Previously, convolutional neural networks (CNNs) were the most commonly used deep learning-based methods for FV authentication. Recently, the vision Transformer (ViT)-based method has started getting attention from the research community due to its excellent performance in many computer vision tasks. In this article, we delve into ViTs and propose a novel model, FV Transformer (FVT), for FV authentication. The FVT consists of four key modules: 1) the conditional position embedding, which is capable of dynamically generating position codes according to the input FV tokens; 2) the weight-shared expanded multilayer perceptron (EMLP), which helps to extract richer and more robust token information; 3) the local information-enhanced feedforward network (FFN), which enhances the ability of local information extraction; and 4) the expansion-less mechanism (ELM) for aggregating adjacent FV tokens, which implements the pyramid structure, and hence, the multilevel feature extraction capability is introduced to the Transformer architecture, which originally focuses on global information. To fully validate the performance and generalization of FVT, experiments were conducted on nine publicly available FV datasets. The effectiveness of each key module of FVT is demonstrated in the ablation experiments. Also, the comparative experiments show that the FVT outperforms several baseline Transformer models and achieves competitive performance when compared with the state-of-the-art (SOTA) FV authentication methods.