Advanced Search
中文
Volume 46 Issue 2
Feb.  2024
Turn off MathJax
Article Contents
Abstract
References
Related Articles
LI Jiayang, ZHOU Yingyue, YANG Yang, LI Xiaoxia. High-Security Finger Vein Recognition System Using Lightweight Neural Network[J]. Infrared Technology , 2024, 46(2): 168-175.
Citation: LI Jiayang, ZHOU Yingyue, YANG Yang, LI Xiaoxia. High-Security Finger Vein Recognition System Using Lightweight Neural Network[J]. Infrared Technology , 2024, 46(2): 168-175.
shu

High-Security Finger Vein Recognition System Using Lightweight Neural Network

  • 1.

    School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China

  • 2.

    Sichuan Provincial Key Laboratory of Robot Technology Used for Special Environment, Southwest University of Science and Technology, Mianyang 621010, China

More Information
  • Received Date: July 29, 2022
  • Revised Date: September 17, 2022
    Graphical Abstract
    • Abstract
  • Special materials can fake finger veins to deceive recognition systems, and the large amount of computation is required for finger vein recognition using convolutional neural networks. In this study, a finger vein recognition system with an in vivo detection function and a lightweight convolutional neural network structure was designed. Changes in finger pulse waves were detected using Photo Plethysmo Graphy to determine whether the object was living. The ResNet-18 convolutional neural network was improved using a pruning and channel recovery method, and L1 regularization was combined to improve the feature selection ability of the convolutional neural network. On the basis of improving the accuracy of the algorithm, the network can effectively reduce the consumption of computing resources. The experimental results show that by using the improved pruning and channel recovery optimization structure, the number of parameters decreases by 75.6%, the amount of calculations decreases by 25.6 %, and the iso-error rates obtained in the Shandong University and Hong Kong Polytechnic University finger vein databases are 0.025% and 0.085%, respectively, which are significantly lower than those of RESNET-18 (0.117% and 0.213%).
    • FullText(HTML)
    • References (23)
  • [1]
    Jain A, Bolle R, Pankanti S. Biometrics: Personal Identification in Networked Sciety[M]. Springer Science & Business Media, 1999.
    [2]
    黄易豪. 基于手部静脉图像的身份识别关键技术研究[D]. 绵阳: 西南科技大学, 2021.

    HUANG Yihao. Research on Key Technologies of Identity Recognition Based on Hand Vein Images[D]. Mianyang: Southwest University of Science and Technology, 2021.
    [3]
    Mohsin A H, Zaidan A A, Zaidan B B, et al. Finger vein biometrics: Taxonomy analysis, open challenges, future directions, and recommended solution for decentralised network architectures[J]. IEEE Access, 2020, 8: 9821-9845. DOI: 10.1109/ACCESS.2020.2964788
    [4]
    Ahmad Radzi S, Khalil-Hani M, Bakhteri R. Finger-vein biometric identification using convolutional neural network[J]. Turkish Journal of Electrical Engineering and Computer Sciences, 2016, 24(3): 1863-1878.
    [5]
    Das R, Piciucco E, Maiorana E, et al. Convolutional neural network for finger-vein-based biometric identification[J]. IEEE Transactions on Information Forensics and Security, 2019, 14(2): 360-373. DOI: 10.1109/TIFS.2018.2850320
    [6]
    HONG H G, Lee M B, Park K R. Convolutional neural network-based finger-vein recognition using NIR image sensors[J]. Sensors, 2017, 17(6): 1297-1318. DOI: 10.3390/s17061297
    [7]
    LI R, SU Z, ZHANG H, et al. Application of improved GCNs in feature representation of finger-vein[J]. Journal of Signal Processing, 2020, 36(4): 550-561.
    [8]
    HE X, CHEN X. Finger vein recognition based on improved convolution neural network[J]. Comput. Eng. Design, 2019, 40: 562-566.
    [9]
    DAI Y, HUANG B, LI W, et al. A method for capturing the finger-vein image using nonuniform intensity infrared light[C]//2008 Congress on Image and Signal Processing, 2008: 501-505.
    [10]
    YU X, YANG W, LIAO Q, et al. A novel finger vein pattern extraction approach for near-infrared image[C]//2009 2nd International Congress on Image and Signal Processing, 2009: 1-5.
    [11]
    HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778.
    [12]
    陈鹤森. 基于深度学习的细粒度图像识别研究[D]. 北京: 北京邮电大学, 2018.

    CHEN Hesen. Research on Fine-Grained Image Recognition Based on Deep Learning[D]. Beijing: Beijing University of Posts and Tele-Communications, 2018.
    [13]
    LIU J, ZHUANG B, ZHUANG Z, et al. Discrimination-aware network pruning for deep model compression[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 44(8): 4035-4051.
    [14]
    HAN K, WANG Y, TIAN Q, et al. Ghostnet: more features from cheap operations[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 1580-1589.
    [15]
    弓雷. ARM嵌入式Linux系统开发详解[M]. 2版: 北京: 清华大学出版社, 2014.

    GONG Lei. ARM Embedded Linux System Development Details[M]. 2nd edition: Beijing: Tsinghua University Publishing House Co. Ltd., 2014.
    [16]
    CHEN X, HUANG M, FU Y. Simultaneous acquisition of near infrared image of hand vein and pulse for liveness dorsal hand vein identification[J]. Infrared Physics & Technology, 2021, 115: 103688.
    [17]
    WU W, YUAN W, LIN S, et al. Selection of typical wavelength for palm vein recognition[J]. Acta Optica Sinica, 2012, 32(12): 1211002. DOI: 10.3788/AOS201232.1211002
    [18]
    LIU J, YAN B P-Y, DAI W-X, et al. Multi-wavelength photo-plethysmography method for skin arterial pulse extraction[J]. Biomedical Optics Express, 2016, 7(10): 4313-4326. DOI: 10.1364/BOE.7.004313
    [19]
    YIN Y, LIU L, SUN X. SDUMLA-HMT: a multimodal biometric database[C]// Chinese Conference on Biometric Recognition(CCBR), 2011: 260-268.
    [20]
    Kumar A, ZHOU Y. Human identification using finger images[J]. IEEE Transactions on Image Processing, 2011, 21(4): 2228-2244.
    [21]
    罗建豪. 面向深度卷积神经网络的模型剪枝算法研究[D]. 南京: 南京大学, 2020.

    LUO Jianhao. Research on Model Pruning Algorithmsfor Deep Convolutional NeuralNetworks[D]. Nanjing: Nanjing University, 2020.
    [22]
    LI H, Kadav A, Durdanovic I, et al. Pruning filters for efficient convnets[J/OL]. arXiv preprint arXiv: 1608.08710, 2016.
    [23]
    HONG H G, Lee M B, Park K R. Convolutional neural network-based finger-vein recognition using NIR image sensors[J]. Sensors, 2017, 17(6): 1297. DOI: 10.3390/s17061297
    • Related Articles

  • Related Articles

    [1]GE Peng, YANG Bo, HONG Wenqing, WANG Xiaodong, LIU Chuanming, SU Lan, SU Junbo. Dynamic Range Compression and Detail Enhancement Algorithm Combined with PE for High Dynamic Range Infrared Images[J]. Infrared Technology , 2020, 42(3): 279-285.
    [2]Research on Stable Tracking Platforms: Disturbance Rejection Method Based on H∞ Robust Control[J]. Infrared Technology , 2017, 39(9): 859-863.
    [3]MEI Jiacheng, WANG Rui, YE Hanmin. Compressive Fusion and Target Detection Based on Sparse Representation[J]. Infrared Technology , 2016, 38(3): 218-224.
    [4]YANG Xue, CHEN Fansheng. Lossless Compression of Infrared Image Based on Prediction and JPEG2000[J]. Infrared Technology , 2016, 38(2): 144-148,156.
    [5]Simulation of Infrared Image Based on Electric Heating Film[J]. Infrared Technology , 2013, (10): 650-653.
    [6]Image Compression Algorithm Based on Wavelet Transform and Visual Sensitivity Characteristics[J]. Infrared Technology , 2013, (3): 155-160.
    [7]LAN Chun-jia, TANG Xin-yi, WANG Shi-yong, LI Shao-xiu. High-speed Infrared Image Acquisition System Based on PCI Express[J]. Infrared Technology , 2011, 33(1): 37-40. DOI: 10.3969/j.issn.1001-8891.2011.01.009
    [8]PANG Zhen-peng, LI Wei, ZHOU Kai. A Layered Algorithm Image Coding Based on Combination of Wavelet and Ridgelet[J]. Infrared Technology , 2009, 31(10): 581-584. DOI: 10.3969/j.issn.1001-8891.2009.10.006
    [9]ZHANG Su-wen, WANG Li-li, MIAO Dan-dan. An Improved Embedded Zerotree Wavelets Image Coding Algorithm[J]. Infrared Technology , 2008, 30(9): 541-545. DOI: 10.3969/j.issn.1001-8891.2008.09.012
    [10]ZHAO Xiu-ying, ZHAI Lin-pei, WEI Jing-yuan, LIU Hong. Integer Biorthogonal Wavelet Transform on Image Compression[J]. Infrared Technology , 2005, 27(6): 458-462. DOI: 10.3969/j.issn.1001-8891.2005.06.006

  • Cited by

    Periodical cited type(8)

    1. 张宇，张健，齐林. 基于遗传算法优化Canny算子的织绣文物纹样抽取方法研究. 丝绸. 2024(06): 1-12 .
    2. 严丹昭，陈晶，兰旺耀，廖一鹏. 基于表层温度深度学习的电缆接头绝缘劣化非接触式诊断. 红外技术. 2024(06): 712-721 . 本站查看
    3. 王鑫刚，田军委，于亚琳，王沁，张杰. 改进Canny算法的红外人脸图像边缘轮廓提取. 应用光学. 2023(01): 61-70 .
    4. 肖建英，邓强. 三维光学模型的激光图像边缘检测研究. 激光杂志. 2023(08): 110-114 .
    5. 涂伟沪，蔡玲霞，李学军. 基于改进蝗虫算法优化Canny算子的鸡蛋裂纹图像检测. 食品与机械. 2022(02): 167-172+202 .
    6. 高海韬，李丹宁，王彬，唐鑫鑫. 运动模糊图像PSF参数估计方法改进及图像复原. 计算机工程. 2022(09): 197-203+212 .
    7. 赵一鸣，胡燕海. 用于小型注塑件边缘提取的改进坎尼算法. 机械制造. 2022(07): 1-5+11 .
    8. 杨林超，张新锋，刘康. 管道机器人采集图像缺陷检测方法研究. 工业仪表与自动化装置. 2021(06): 48-51+64 .

    Other cited types(5)

Catalog

    Get Citation
    PDF
    XML
    Article views (102) PDF downloads (37) Cited by(13)
    Related

    Copyright © 《Infrared Technology》Editorial Office滇ICP备12000354号-3

    Supported by: Beijing Renhe Information Technology Co., Ltd.  Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return