基于注意力机制的轻量级双卷积手指静脉识别网络

赵冰艳; 梁义怀; 张中霞; 张文政

doi:10.11999/JEIT250380

基于注意力机制的轻量级双卷积手指静脉识别网络

doi: 10.11999/JEIT250380 cstr: 32379.14.JEIT250380

赵冰艳^1,,
梁义怀^1, ,,
张中霞²,
张文政^{2, 3}

1.
西南交通大学信息科学与技术学院成都 611756
2.
中国电子科技网络信息安全有限公司成都 610041
3.
保密通信全国重点研究所成都 610041

基金项目: 中央高校基本科研业务费(2682024CX031)

详细信息

作者简介:
赵冰艳：女，硕士生，研究方向为手指静脉图像识别

梁义怀：男，讲师，研究方向为数据安全与隐私保护、群智感知、联邦学习

张中霞：女，博士，研究方向为信息安全、图像处理

张文政：男，高级工程师，研究方向为密码学理论和技术

通讯作者:
梁义怀　liangyh@swjtu.edu.cn

中图分类号: TN02;TP389
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出版历程
- 收稿日期: 2025-05-07
- 修回日期: 2026-01-06
- 录用日期: 2026-01-06
- 网络出版日期: 2026-01-15

Lightweight Dual Convolutional Finger Vein Recognition Network Based on Attention Mechanism

1.
School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China
2.
China Electronis Technology Cyber Security Co., Lt, Chengdu 610041, China
3.
Science and Technology on Communication Security Laboratory, Chengdu 610041, China

Funds: The Fundamental Research Funds for the Central Universities(2682024CX031)

摘要

摘要: 基于深度学习的指静脉识别方法已被广泛应用于生物特征识别领域，然而现有模型普遍存在复杂度与分类性能失衡的问题，难以在内存受限和计算资源稀缺环境下高效完成识别任务。针对上述问题，该文提出了一种融合注意力机制的轻量化双通道卷积神经网络模型。该模型设计有双分支协同架构，旨在分别提取核心特征与辅助特征，从而丰富特征集合并增强网络对远程依赖特征的捕捉能力。通过设计一种并行双重注意力机制，以促进融合特征间的信息交互，引导模型聚焦于高价值信息，学习更具区分度的特征表示。实验结果显示，此模型在USM、HKPU和SDUMLA3个公开数据集上的识别准确率分别达到99.70%、98.33%和98.27%，比现有先进方法分别提升2.34%、1.79%和2.03%，而参数量减少11.35%～60.19%，表明提出的双卷积模型实现了网络规模与识别准确率之间的有效平衡。
- 指静脉识别 /
- 双卷积模型 /
- 特征表示 /
- 轻量化 /
- 并行双重注意力机制
Abstract: Objective Finger vein recognition is an emerging biometric authentication technology valued for its physiological uniqueness and advantages in in vivo detection. However, mainstream deep learning recognition frameworks still face two challenges. High-precision recognition often depends on complex network structures, which increase parameter counts and hinder deployment in memory-limited embedded devices and edge scenarios with constrained computing resources. Model compression can reduce computational cost but often weakens feature representation, creating a conflict between recognition accuracy and efficiency. To address these issues, a lightweight dual convolutional model integrated with an attention mechanism is proposed. A parallel heterogeneous convolution module and an attention guidance mechanism are designed to extract diverse image features and improve recognition accuracy while preserving a lightweight network structure. Methods The proposed architecture adopts a three-level collaborative mechanism comprising feature extraction, dynamic calibration, and decision fusion. A dual convolutional feature extraction module is constructed using normalized ROI images. This module combines heterogeneous convolution kernels. Rectangular convolution branches with different shapes capture venous topological structures and diameter orientations, whereas square convolution branches employ stacked square kernels to extract local texture details and background intensity distributions. These branches operate in parallel with reduced channel numbers and generate complementary responses through kernel shape diversity. This design reduces parameter scale while improving feature discrimination. A parallel dual attention mechanism is then applied to achieve two-dimensional calibration through joint optimization of channel attention and spatial attention. Channel attention adaptively assigns weights to enhance discriminative venous texture features, whereas spatial attention constructs pixel-level dependency models that focus on effective discriminative regions. A parallel concatenation fusion strategy preserves structural information without introducing additional parameters and improves sensitivity to critical features. Finally, a three-level progressive feature optimization structure is implemented. A convolutional compression module with stride 2 nests multi-scale receptive fields and progressively refines primary features during dimensionality reduction. Two fully connected layers then perform feature space transformation. The first layer applies ReLU activation to form sparse representations, and the final layer applies Softmax for probability calibration. This structure balances shallow underfitting and deep overfitting while maintaining efficient forward inference. Results and Discussions The effectiveness and robustness of the proposed network are evaluated on three public datasets, namely USM, HKPU, and SDUMLA. Recognition accuracy is assessed using the Acc metric. Experimental results (Table 1) show strong recognition performance. Feature visualization heatmaps (Fig. 4, Fig. 6) confirm that the network extracts complete and discriminative venous features. Training visualizations (Fig. 7, Fig. 8) show stable loss and accuracy trends, achieving 100% classification performance and demonstrating training reliability and robustness. Quantitative comparisons (Tables 2 and 3) indicate that the proposed method effectively addresses the trade-off between model complexity and classification performance and achieves superior results across all three datasets. Ablation studies (Table 4) further verify the effectiveness of the proposed modules and show significant improvements in finger vein recognition performance. Conclusions A lightweight dual convolutional neural network with an attention mechanism is proposed. The network consists of three core modules: a dual convolutional feature extraction module, a parallel dual-attention module, and a feature optimization classification module. During feature extraction, long-range venous features and background information are jointly encoded through a low-channel parallel design, which substantially reduces parameter counts while improving inter-individual discrimination. The attention module efficiently captures critical venous features without the parameter expansion commonly observed in conventional attention mechanisms. The feature optimization classification module applies progressive feature recalibration, which reduces underfitting and overfitting during stacked dimensionality reduction. Experimental results show recognition accuracies of 99.70%, 98.33%, and 98.27% on the USM, HKPU, and SDUMLA datasets, corresponding to an average improvement of 2.05% over existing state-of-the-art methods. Compared with representative lightweight finger vein recognition approaches, the proposed method reduces parameter scale by 11.35%～60.19%, achieving a balance between model lightening and performance improvement.
- Finger vein recognition /
- Dual convolutional model /
- Feature representation /
- Lightweight /
- Parallel dual attention mechanism

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图 1 手指静脉识别流程图和现有方法的问题

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图 2 ALDCNet算法整体结构

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图 3 DCFE模块结构

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图 4 矩形卷积和方形卷积作用下输出特征的热图

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图 5 PDA模块结构

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图 6 Grad-CAM热图

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图 7 两个公共数据集的损失曲线和准确率曲线

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图 8 混淆矩阵图

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表 1 不同卷积核组合在3个数据集上的识别准确率(%)

矩形卷积核	方形卷积核(USM)			方形卷积核(HKPU)			方形卷积核(SDUMLA)
矩形卷积核	5×5	7×7	9×9	5×5	7×7	9×9	5×5	7×7	9×9
1×3	99.49	99.59	99.39	97.14	97.62	98.33	97.64	97.48	97.80
1×5	99.70	99.39	99.49	97.38	97.62	98.10	97.32	97.16	98.27
1×7	99.29	99.29	98.78	97.86	97.86	97.62	97.48	97.64	97.80
注：粗体表示所有方案中性能指标的最高值。

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表 2 与其他方法性能指标的对比(%)

方法	Acc(USM)	EER(USM)	Acc(HKPU)	EER(HKPU)	Acc(SDUMLA)	EER(SDUMLA)
AlexNet^[12]	97.26	0.10	86.90	3.99	92.45	1.10
VGG16^[13]	95.93	0.31	76.67	4.78	78.14	1.73
Liu等人^[14]	97.97	0.20	77.14	4.24	69.65	4.56
LALBP^[15]	79.47	–	73.10	–	76.89	–
MMNBP^[16]	83.29	–	75.16	–	80.00	–
ViT-Cap^[17]	98.33	0.28	96.18	1.66	93.79	1.30
Lu等人^[18]	91.76	2.46	90.98	3.89	94.12	0.94
ALA Net^[19]	97.74	0.34	97.86	0.32	94.50	0.53
NLNet^[20]	98.98	0.48	96.22	0.55	94.30	1.13
ALDCNet	99.70	0.08	98.33	0.28	98.27	0.35
注：粗体表示所有方法中性能指标的最高值。

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表 3 模型参数配置的对比

方法	准确率(%)	卷积核数	操作大小	卷积层数	参数量(M)	时间(ms)
AlexNet^[12]	97.26	64,192,384,256×2	11×11,5×5,3×3,3×3	5	2.454	1.01
VGG16^[13]	95.93	64×2,128×2,256×3,512×3,512×3	3×3,3×3,3×3,3×3,3×3	13	14.714	3.00
Liu等人^[14]	97.97	64,64,64,64,64	5×5,5×5,5×5,5×5,5×5	5	0.412	1.22
ALDCNet	99.70	32×2,32×3,32×3,32×3,64	3×3,1×5,5×5,3×3,3×3	12	0.164	1.66
注：粗体表示最优值。

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表 4 消融实验(%)

模型	Acc (USM)	EER (USM)	Acc (HKPU)	EER (HKPU)	Acc (SDUMLA)	EER (SDUMLA)
无PDA	99.09	0.20	97.38	0.52	96.54	0.47
无DCFE-RC	99.29	0.10	97.38	0.48	96.69	0.64
无DCFE-SC	99.39	0.10	97.86	0.48	97.01	0.63
ALDCNet	99.70	0.08	98.33	0.28	98.27	0.35

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参考文献(20)

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