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.

High-Security Finger Vein Recognition System Using Lightweight Neural Network

More Information
  • Received Date: July 29, 2022
  • Revised Date: September 17, 2022
  • 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%).
  • [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

    [1]DAI Yueming, YANG Lufeng, TONG Xiongmin. Real-time Section State Verification Method of Energy Management System Low Voltage Equipment Based on Infrared Image and Deep Learning[J]. Infrared Technology , 2024, 46(12): 1464-1470.
    [2]XU Guangxian, WANG Zemin, MA Fei. Hyperspectral Mixed Noise Image Restoration Based on Non-Convex Low-Rank Tensor Decomposition and Group Sparse Total Variation[J]. Infrared Technology , 2024, 46(9): 1025-1034.
    [3]DUAN Jin, ZHANG Hao, SONG Jingyuan, LIU Ju. Review of Polarization Image Fusion Based on Deep Learning[J]. Infrared Technology , 2024, 46(2): 119-128.
    [4]WU Lingxiao, KANG Jiayin, JI Yunxiang. Infrared and Visible Image Fusion Based on Guided Filter and Sparse Representation in NSST Domain[J]. Infrared Technology , 2023, 45(9): 915-924.
    [5]LONG Zhiliang, DENG Yueming, WANG Runmin, DONG Jun. Infrared and Visible Image Fusion Based on Saliency Detection and Latent Low-Rank Representation[J]. Infrared Technology , 2023, 45(7): 705-713.
    [6]SUN Bin, ZHUGE Wuwei, GAO Yunxiang, WANG Zixuan. Infrared and Visible Image Fusion Based on Latent Low-Rank Representation[J]. Infrared Technology , 2022, 44(8): 853-862.
    [7]ZHANG Yutong, ZHAI Xuping, NIE Hong. Deep Learning Method for Action Recognition Based on Low Resolution Infrared Sensors[J]. Infrared Technology , 2022, 44(3): 286-293.
    [8]MEI Jiacheng, WANG Rui, YE Hanmin. Compressive Fusion and Target Detection Based on Sparse Representation[J]. Infrared Technology , 2016, 38(3): 218-224.
    [9]SONG Bin, WU Le-hua, TANG Xiao-jie, WEN Yu-qiang, MOU Yu-fei. An Image Fusion Algorithm Based on DCT Sparse Representation and Dual-PCNN[J]. Infrared Technology , 2015, (4): 283-288.
    [10]SUN Jun-ding, ZHAO Hui-hui. Sparse Representation and Applications in Image Processing[J]. Infrared Technology , 2014, (7): 533-537.

Catalog

    Article views (96) PDF downloads (37) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return