Wei Jia, Jian Gao, Wei Xia, Yang Zhao, Hai Min, Jing-Ting Lu. A Performance Evaluation of Classic Convolutional Neural Networks for 2D and 3D Palmprint and Palm Vein Recognition. International Journal of Automation and Computing, vol. 18, no. 1, pp.18-44, 2021. https://doi.org/10.1007/s11633-020-1257-9
Citation: Wei Jia, Jian Gao, Wei Xia, Yang Zhao, Hai Min, Jing-Ting Lu.

A Performance Evaluation of Classic Convolutional Neural Networks for 2D and 3D Palmprint and Palm Vein Recognition

. International Journal of Automation and Computing, vol. 18, no. 1, pp.18-44, 2021. https://doi.org/10.1007/s11633-020-1257-9

A Performance Evaluation of Classic Convolutional Neural Networks for 2D and 3D Palmprint and Palm Vein Recognition

doi: 10.1007/s11633-020-1257-9
More Information
  • Author Bio:

    Wei Jia received the B. Sc. degree in informatics from Central China Normal University, China in 1998, the M. Sc. degree in computer science from Hefei University of Technology, China in 2004, and the Ph. D. degree in pattern recognition and intelligence system from University of Science and Technology of China, China in 2008. He has been a research associate professor in Hefei Institutes of Physical Science, Chinese Academy of Sciences, China from 2008 to 2016. He is currently an associate professor in Key Laboratory of Knowledge Engineering with Big Data, Ministry of Education, and in School of Computer Science and Information Engineering, Hefei University of Technology, China. His research interests include computer vision, biometrics, pattern recognition, image processing and machine learning. E-mail: jiawei@hfut.edu.cn (Corresponding author) ORCID iD: 0000-0001-5628-6237

    Jian Gao received the B. Sc. degree in mechanical design and manufacturing and automation from Hefei University of Technology, China in 2018. Now, he is currently a master student in School of Computer Science and Information Engineering, Hefei University of Technology, China. His research interests include computer vision, biometrics recognition and deep learning. E-mail: 787117010@qq.com

    Wei Xia received the B. Sc. degree in computer science from Anhui University of Science and Technology, China in 2018. He is a master student in School of Computer Science and Information Engineering, Hefei University of Technology, China. His research interests include biometrics, pattern recognition and image processing. E-mail: hewelxw@mail.hfut.edu.cn

    Yang Zhao received the B. Eng. and Ph.D. degrees in pattern recognition and intelligence from Department of Automation, University of Science and Technology of China, China in 2008 and 2013. From 2013 to 2015, he was a postdoctoral researcher at School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School, China. Currently, he is an associate professor at School of Computer Science and Information Engineering, Hefei University of Technology, China. His research interests include image processing and computer vision. E-mail: yzhao@hfut.edu.cn

    Hai Min received the Ph. D. degree in pattern recognition and intelligence system from University of Science and Technology of China, China in 2014. He is currently an associate professor in School of Computer Science and Information Engineering, Hefei University of Technology, China. His research interests include pattern recognition and image segmentation. E-mail: minhai361@aliyun.com

    Jing-Ting Lu received the B. Sc., M. Sc. and Ph. D. degrees in computer science from Hefei University of Technology, China in 2004, 2009, and 2014, respectively. She is currently a lector in School of Computer and Information, Hefei University of Technology, China. Her research interests include computer vision, biometrics, pattern recognition, image processing and machine learning. E-mail: lujt@hfut.edu.cnORCID iD: 0000-0002-0210-7149

  • Received Date: 2020-08-06
  • Accepted Date: 2020-09-25
  • Publish Online: 2020-12-29
  • Publish Date: 2021-02-18
  • Palmprint recognition and palm vein recognition are two emerging biometrics technologies. In the past two decades, many traditional methods have been proposed for palmprint recognition and palm vein recognition, and have achieved impressive results. However, the research on deep learning-based palmprint recognition and palm vein recognition is still very preliminary. In this paper, in order to investigate the problem of deep learning based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct performance evaluation of seventeen representative and classic convolutional neural networks (CNNs) on one 3D palmprint database, five 2D palmprint databases and two palm vein databases. A lot of experiments have been carried out in the conditions of different network structures, different learning rates, and different numbers of network layers. We have also conducted experiments on both separate data mode and mixed data mode. Experimental results show that these classic CNNs can achieve promising recognition results, and the recognition performance of recently proposed CNNs is better. Particularly, among classic CNNs, one of the recently proposed classic CNNs, i.e., EfficientNet achieves the best recognition accuracy. However, the recognition performance of classic CNNs is still slightly worse than that of some traditional recognition methods.

     

  • loading
  • [1]
    S. G. Tong, Y. Y. Huang, Z. M. Tong. A robust face recognition method combining LBP with multi-mirror symmetry for images with various face interferences. International Journal of Automation and Computing, vol. 16, no. 5, pp. 671–682, 2019. DOI: 10.1007/s11633-018-1153-8.
    [2]
    D. Zhang, W. M. Zuo, F. Yue. A comparative study of palmprint recognition algorithms. ACM Computing Surveys, vol. 44, no. 1, Article number 2, 2012. DOI: 10.1145/2071389.2071391.
    [3]
    L. K. Fei, G. M. Lu, W. Jia, S. H. Teng, D. Zhang. Feature extraction methods for palmprint recognition: A survey and evaluation. IEEE Transactions on Systems,Man,and Cybernetics:Systems, vol. 49, no. 2, pp. 346–363, 2019. DOI: 10.1109/TSMC.2018.2795609.
    [4]
    D. X. Zhong, X. F. Du, K. C. Zhong. Decade progress of palmprint recognition: A brief survey. Neurocomputing, vol. 328, pp. 16–28, 2019. DOI: 10.1016/j.neucom.2018.03.081.
    [5]
    L. Zhang, Y. Shen, H. Y. Li, J. W. Lu. 3D palmprint identification using block-wise features and collaborative representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 8, pp. 1730–1736, 2015. DOI: 10.1109/TPAMI.2014.2372764.
    [6]
    W. X. Kang, Q. X. Wu. Contactless palm vein recognition using a mutual foreground-based local binary pattern. IEEE Transactions on Information Forensics and Security, vol. 9, no. 11, pp. 1974–1985, 2014. DOI: 10.1109/TIFS.2014.2361020.
    [7]
    B. Hu, J. C. Wang. Deep learning based hand gesture recognition and UAV flight controls. International Journal of Automation and Computing, vol. 17, no. 1, pp. 17–29, 2020. DOI: 10.1007/s11633-019-1194-7.
    [8]
    V. K. Ha, J. C. Ren, X. Y. Xu, S. Zhao, G. Xie, V. Masero, A. Hussain. Deep learning based single image super-resolution: A survey. International Journal of Automation and Computing, vol. 16, no. 4, pp. 413–426, 2019. DOI: 10.1007/s11633-019-1183-x.
    [9]
    K. Sundararajan, D. L. Woodard. Deep learning for biometrics: A survey. ACM Computing Surveys, vol. 51, no. 3, Article number 65, 2018. DOI: 10.1145/3190618.
    [10]
    L. K. Fei, B. Zhang, W. Jia, J. Wen, D. Zhang. Feature extraction for 3-D palmprint recognition: A survey. IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 3, pp. 645–656, 2020. DOI: 10.1109/TIM.2020.2964076.
    [11]
    D. Zhang, W. K. Kong, J. You, M. Wong. Online palmprint identification. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 9, pp. 1041–1050, 2003. DOI: 10.1109/TPAMI.2003.1227981.
    [12]
    D. Zhang, Z. H. Guo, G. M. Lu, L. Zhang, W. M. Zuo. An online system of multispectral palmprint verification. IEEE Transactions on Instrumentation and Measurement, vol. 59, no. 2, pp. 480–490, 2010. DOI: 10.1109/TIM.2009.2028772.
    [13]
    W. Jia, B. Zhang, J. T. Lu, Y. H. Zhu, Y. Zhao, W. M. Zuo, H. B. Ling. Palmprint recognition based on complete direction representation. IEEE Transactions on Image Processing, vol. 26, no. 9, pp. 4483–4498, 2017. DOI: 10.1109/TIP.2017.2705424.
    [14]
    W. Jia, R. X. Hu, J. Gui, Y. Zhao, X. M. Ren. Palmprint recognition across different devices. Sensors, vol. 12, no. 6, pp. 7938–7964, 2012. DOI: 10.3390/s120607938.
    [15]
    L. Zhang, L. D. Li, A. Q. Yang, Y. Shen, M. Yang. Towards contactless palmprint recognition: A novel device, a new benchmark, and a collaborative representation based identification approach. Pattern Recognition, vol. 69, pp. 199–212, 2017. DOI: 10.1016/j.patcog.2017.04.016.
    [16]
    W. Li, D. Zhang, L. Zhang, G. M. Lu, J. Q. Yan. 3-D palmprint recognition with joint line and orientation features. IEEE Transactions on Systems,Man,and Cybernetics – Part C:Applications and Reviews, vol. 41, no. 2, pp. 274–279, 2011. DOI: 10.1109/TSMCC.2010.2055849.
    [17]
    L. Zhang, Z. X. Cheng, Y. Shen, D. Q. Wang. Palmprint and palmvein recognition based on DCNN and a new large-scale contactless palmvein dataset. Symmetry, vol. 10, no. 4, Article number 78, 2018. DOI: 10.3390/sym10040078.
    [18]
    D. S. Huang, W. Jia, D. Zhang. Palmprint verification based on principal lines. Pattern Recognition, vol. 41, no. 4, pp. 1316–1328, 2008. DOI: 10.1016/j.patcog.2007.08.016.
    [19]
    D. Palma, P. L. Montessoro, G. Giordano, F. Blanchini. Biometric palmprint verification: A dynamical system approach. IEEE Transactions on Systems,Man,and Cybernetics:Systems, vol. 49, no. 12, pp. 2676–2687, 2019. DOI: 10.1109/TSMC.2017.2771232.
    [20]
    W. Nie, B. Zhang, S. P. Zhao. Discriminative local feature for hyperspectral hand biometrics by adjusting image acutance. Applied Sciences, vol. 9, no. 19, Article number 4178, 2019. DOI: 10.3390/app9194178.
    [21]
    W. Jia, R. X. Hu, Y. K. Lei, Y. Zhao, J. Gui. Histogram of oriented lines for palmprint recognition. IEEE Transactions on Systems,Man,and Cybernetics:Systems, vol. 44, no. 3, pp. 385–395, 2014. DOI: 10.1109/TSMC.2013.2258010.
    [22]
    Y. T. Luo, L. Y. Zhao, B. Zhang, W. Jia, F. Xue, J. T. Lu, Y. H. Zhu, B. Q. Xu. Local line directional pattern for palmprint recognition. Pattern Recognition, vol. 50, pp. 26–44, 2016. DOI: 10.1016/j.patcog.2015.08.025.
    [23]
    G. Li, J. Kim. Palmprint recognition with Local Micro-structure Tetra Pattern. Pattern Recognition, vol. 61, pp. 29–46, 2017. DOI: 10.1016/j.patcog.2016.06.025.
    [24]
    L. K. Fei, B. Zhang, Y. Xu, D. Huang, W. Jia, J. Wen. Local discriminant direction binary pattern for palmprint representation and recognition. IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 2, pp. 468–481, 2020. DOI: 10.1109/TCSVT.2019.2890835.
    [25]
    L. K. Fei, B. Zhang, Y. Xu, Z. H. Guo, J. Wen, W. Jia. Learning discriminant direction binary palmprint descriptor. IEEE Transactions on Image Processing, vol. 28, no. 8, pp. 3808–3820, 2019. DOI: 10.1109/TIP.2019.2903307.
    [26]
    L. K. Fei, B. Zhang, W. Zhang, S. H. Teng. Local apparent and latent direction extraction for palmprint recognition. Information Sciences, vol. 473, pp. 59–72, 2019. DOI: 10.1016/j.ins.2018.09.032.
    [27]
    X. Q. Wu, Q. S. Zhao. Deformed palmprint matching based on stable regions. IEEE Transactions on Image Processing, vol. 24, no. 12, pp. 4978–4989, 2015. DOI: 10.1109/TIP.2015.2478386.
    [28]
    Z. A. Sun, T. N. Tan, Y. H. Wang, S. Z. Li. Ordinal palmprint represention for personal identification. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, San Diego, USA, pp. 279–284, 2005. DOI: 10.1109/CVPR.2005.267.
    [29]
    W. Jia, D. S. Huang, D. Zhang. Palmprint verification based on robust line orientation code. Pattern Recognition, vol. 41, no. 5, pp. 1504–1513, 2008. DOI: 10.1016/j.patcog.2007.10.011.
    [30]
    Z. H. Guo, D. Zhang, L. Zhang, W. M. Zuo. Palmprint verification using binary orientation co-occurrence vector. Pattern Recognition Letters, vol. 30, no. 13, pp. 1219–1227, 2009. DOI: 10.1016/j.patrec.2009.05.010.
    [31]
    L. K. Fei, Y. Xu, W. L. Tang, D. Zhang. Double-orientation code and nonlinear matching scheme for palmprint recognition. Pattern Recognition, vol. 49, pp. 89–101, 2016. DOI: 10.1016/j.patcog.2015.08.001.
    [32]
    G. M. Lu, D. Zhang, K. Q. Wang. Palmprint recognition using eigenpalms features. Pattern Recognition Letters, vol. 24, no. 9–10, pp. 1463–1467, 2003. DOI: 10.1016/S0167-8655(02)00386-0.
    [33]
    X. Q. Wu, D. Zhang, K. Q. Wang. Fisherpalms based palmprint recognition. Pattern Recognition Letters, vol. 24, no. 15, pp. 2829–2838, 2003. DOI: 10.1016/S0167-8655(03)00141-7.
    [34]
    J. Yang, A. F. Frangi, J. Y. Yang, D. Zhang, Z. Jin. KPCA plus LDA: A complete kernel fisher discriminant framework for feature extraction and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 2, pp. 230–244, 2005. DOI: 10.1109/TPAMI.2005.33.
    [35]
    D. Zhang, G. M. Lu, W. Li, L. Zhang, N. Luo. Palmprint recognition using 3-D information. IEEE Transactions on Systems Man,and Cybernetics – Part C:Applications and Reviews, vol. 39, no. 5, pp. 505–519, 2009. DOI: 10.1109/TSMCC.2009.2020790.
    [36]
    B. Yang, X. H. Wang, J. L. Yao, X. Yang, W. H. Zhu. Efficient local representations for three-dimensional palmprint recognition. Journal of Electronic Imaging, vol. 22, no. 4, Article number 043040, 2013. DOI: 10.1117/1.JEI.22.4.043040.
    [37]
    L. K. Fei, S. H. Teng, J. G. Wu, Y. Xu, J. Wen, C. W. Tian. Combining enhanced competitive code with compacted ST for 3D palmprint recognition. In Proceedings of the 4th IAPR Asian Conference on Pattern Recognition, IEEE, Nanjing, China, pp. 483–487, 2017.
    [38]
    L. K. Fei, G. M. Lu, W. Jia, J. Wen, D. Zhang. Complete binary representation for 3-D palmprint recognition. IEEE Transactions on Instrumentation and Measurement, vol. 67, no. 12, pp. 2761–2771, 2018. DOI: 10.1109/TIM.2018.2830858.
    [39]
    L. Fei, B. Zhang, Y. Xu, W. Jia, J. Wen, J. G. Wu. Precision direction and compact surface type representation for 3D palmprint identification. Pattern Recognition, vol. 87, pp. 237–247, 2019. DOI: 10.1016/j.patcog.2018.10.018.
    [40]
    Y. B. Zhang, Q. Li, J. You, P. Bhattacharya. Palm vein extraction and matching for personal authentication. In Proceedings of the 9th International Conference on Advances in Visual Information Systems, Springer, Shanghai, China, vol. 4781, pp. 154–164, 2007.
    [41]
    L. Mirmohamadsadeghi, A. Drygajlo. Palm vein recognition with local texture patterns. IET Biometrics, vol. 3, no. 4, pp. 198–206, 2014. DOI: 10.1049/iet-bmt.2013.0041.
    [42]
    ManMohan, J. Saxena, K. Teckchandani, P. Pandey, M. K. Dutta, C. M. Travieso, J. B. Alonso-Hernández. Palm vein recognition using local tetra patterns. In Proceedings of the 4th International Work Conference on Bioinspired Intelligence, IEEE, San Sebastian, Spain, pp. 151–156, 2015.
    [43]
    W. X. Kang, Y. Liu, Q. X. Wu, X. S. Yue. Contact-free palm-vein recognition based on local invariant features. PLoS ONE, vol. 9, no. 5, Article number e97548, 2014. DOI: 10.1371/journal.pone.0097548.
    [44]
    Y. B. Zhou, A. Kumar. Human identification using palm-vein images. IEEE Transactions on Information Forensics and Security, vol. 6, no. 4, pp. 1259–1274, 2011. DOI: 10.1109/TIFS.2011.2158423.
    [45]
    S. Elnasir, S. M. Shamsuddin. Proposed scheme for palm vein recognition based on linear discrimination analysis and nearest neighbour classifier. In Proceedings of International Symposium on Biometrics and Security Technologies, IEEE, Kuala Lumpur, Malaysia, pp. 67–72, 2014.
    [46]
    J. X. Xu. An online biometric identification system based on two dimensional Fisher linear discriminant. In Proceedings of the 8th International Congress on Image and Signal, IEEE, Shenyang, China, pp. 894–898, 2015. DOI: 10.1109/CISP.2015.7408004.
    [47]
    Y. P. Lee. Palm vein recognition based on a modified (2D)2 LDA. Signal, Image and Video Processing, vol. 9, no. 1, pp. 229–242, 2015.
    [48]
    B. H. Shekar, N. Harivinod. Multispectral palmprint matching based on joint sparse representation. In Proceedings of the 4th National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, IEEE, Jodhpur, India, 2013.
    [49]
    Y. Lecun, L. Bottou, Y. Bengio, P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2323, 1998. DOI: 10.1109/5.726791.
    [50]
    A. Krizhevsky, I. Sutskever, G. E. Hinton. ImageNet classification with deep convolutional neural networks. In Proceedings of the 25th International Conference on Neural Information Processing Systems, ACM, Red Hook, USA, pp. 1097–1105, 2012.
    [51]
    M. D. Zeiler, R. Fergus. Visualizing and understanding convolutional networks. In Proceedings of the 13th European Conference on Computer Vision, Springer, Zurich, Switzerland, vol. 8689, pp. 818–833, 2014.
    [52]
    M. Lin, Q. Chen, S. Yan. Network in network. In Proceedings of the 2nd International Conference on Learning Representations, Banff, Canada, 2014.
    [53]
    K. Simonyan, A. Zisserman. Very deep convolutional networks for large-scale image recognition. In Proceedings of the 3rd International Conference on Learning Representations, San Diego, USA 2015.
    [54]
    C. Szegedy, W. Liu, Y. Q. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Boston, USA, pp. 1–9, 2015.
    [55]
    S. Ioffe, C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Proceedings of the 32nd International Conference on Machine Learning, Lille, France, pp. 448–456, 2015.
    [56]
    C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Las Vegas, USA, pp. 2818–2826, 2016.
    [57]
    C. Szegedy, S. Ioffe, V. Vanhoucke, A. A. Alemi. Inception-v4, inception-ResNet and the impact of residual connections on learning. In Proceedings of the 31st AAAI Conference on Artificial Intelligence, San Francisco, USA, pp. 4278–4284, 2017.
    [58]
    K. M. He, X. Y. Zhang, S. Q. Ren, J. Sun. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Las Vegas, USA, pp. 770–778, 2016.
    [59]
    G. Huang, Z. Liu, L. Van Der Maaten, K. Q. Weinberger. Densely connected convolutional networks. In Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Honolulu, USA, pp. 2261–2269, 2017.
    [60]
    F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, K. Keutzer. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 0.5 MB model size. [Online], Available: https://arxiv.org/abs/1602.07360, 2017.
    [61]
    A. G. Howard, M. L. Zhu, B. Chen, D. Kalenichenko, W. J. Wang, T. Weyand, M. Andreetto, H. Adam. MobileNets: Efficient convolutional neural networks for mobile vision applications. [Online], Available: https://arxiv.org/abs/1704.04861, 2017.
    [62]
    M. Sandler, A. Howard, M. L. Zhu, A. Zhmoginov, L. C. Chen. MobileNetV2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Salt Lake City, USA, pp. 4510–4520, 2018.
    [63]
    A. Howard, M. Sandler, B. Chen. W. J. Wang, L. C. Chen, M. X. Tan, G. Chu, V. Vasudevan, Y. K. Zhu, R. M. Pang, H, Adam, Q. Le. Searching for mobileNetV3. In Proceedings of IEEE/CVF International Conference on Computer Vision, IEEE, Seoul, South Korea, pp. 1314–1324, 2019.
    [64]
    X. Y. Zhang, X. Y. Zhou, M. X. Lin, J. Sun. ShuffleNet: An extremely efficient convolutional neural network for mobile devices. In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Salt Lake City, USA, pp. 6848–6856, 2018.
    [65]
    N. N. Ma, X. Y. Zhang, H. T. Zheng, J. Sun. Shufflenet V2: Practical guidelines for efficient CNN architecture design. In Proceedings of the 15th European Conference on Computer Vision, Springer, Munich, Germany, vol. 11218, pp. 122–138, 2018.
    [66]
    F. Chollet. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Honolulu, USA, pp. 1800–1807, 2017.
    [67]
    A. Gholami, K. Kwon, B. Wu, Z. Z. Tai, X. Y. Yue, P. Jin, S. C. Zhao, K. Keutzer. SqueezeNext: Hardware-aware neural network design. In Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, IEEE, Salt Lake City, USA, pp. 1719–1728, 2018.
    [68]
    S. N. Xie, R. Girshick, P. Dollár, Z. W. Tu, K. M. He. Aggregated residual transformations for deep neural networks. In Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Honolulu, USA, pp. 5987–5995, 2017.
    [69]
    J. Hu, L. Shen, G. Sun. Squeeze-and-excitation networks. In Proceedings of IEEE/CVF Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, Salt Lake City, USA, pp. 7132–7141, 2018.
    [70]
    M. X. Tan, Q. Le. EfficientNet: Rethinking model scaling for convolutional neural networks. In Proceedings of the 36th International Conference on Machine Learning, Long Beach, USA, pp. 10691–10700, 2019.
    [71]
    K. Han, Y. H. Wang, Q. Tian, J. Y. Guo, C. J. Xu, C. Xu. GhostNet: More features from cheap operations. [Online], Available: https://arxiv.org/abs/1911.11907, 2019.
    [72]
    I. Radosavovic, R. P. Kosaraju, R. Girshick, K. M. He, P. Dollár. Designing network design spaces. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Seattle, USA, pp. 10428–10436, 2020.
    [73]
    H. Zhang, C. R. Wu, Z. Y. Zhang, Y. Zhu, Z. Zhang, H. B. Lin, Y. Sun, T. He, J. Mueller, R. Manmatha, M. Li, A. Smola. ResNeSt: Split-attention networks. [Online], Available: https://arxiv.org/abs/2004.08955, 2020.
    [74]
    A. Jalali, R. Mallipeddi, M. Lee. Deformation invariant and contactless palmprint recognition using convolutional neural network. In Proceedings of the 3rd International Conference on Human-agent Interaction, ACM, Daegu, Korea, pp. 209–212, 2015.
    [75]
    D. D. Zhao, X. Pan, X. L. Luo, X. J. Gao. Palmprint recognition based on deep learning. In Proceedings of the 6th International Conference on Wireless, Mobile and Multi-Media, IEEE, Beijing, China, pp. 214–216, 2015.
    [76]
    S. Minaee, Y. Wang. Palmprint recognition using deep scattering convolutional network.[Online], Available: https://arxiv.org/abs/1603.09027, 2016.
    [77]
    D. Liu, D. M. Sun. Contactless palmprint recognition based on convolutional neural network. In Proceedings of the 13th IEEE International Conference on Signal Processing, IEEE, Chengdu, China, pp. 1363–1367, 2017.
    [78]
    J. Svoboda, J. Masci, M. M. Bronstein. Palmprint recognition via discriminative index learning. In Proceedings of the 23rd International Conference on Pattern Recognition, IEEE, Cancun, Mexico, pp. 4232–4237, 2016.
    [79]
    A. Q. Yang, J. X. Zhang, Q. L. Sun, Q. Zhang. Palmprint recognition based on CNN and local coding features. In Proceedings of the 6th International Conference on Computer Science and Network Technology, IEEE, Dalian, China, pp. 482–487, 2018.
    [80]
    A. Meraoumia, F. Kadri, H. Bendjenna, S. Chitroub, A. Bouridane. Improving biometric identification performance using pcanet deep learning and multispectral palmprint. Biometric Security and Privacy: Opportunities & Challenges in the Big Data Era, R. Jiang, S. Al-maadeed, A. Bouridane, P. D. Crookes, A. Beghdadi Eds., Cham, Switzerland: Springer, pp. 51–69, 2017.
    [81]
    D. Zhong, Y. Yang, X. Du. Palmprint recognition using siamese network. In Proceedings of the 13th Chinese Conference on Biometric Recognition, Springer, Urumqi, China, vol. 10996, pp. 48–55, 2018.
    [82]
    A. Michele, V. Colin, D. D. Santika. MobileNet convolutional neural networks and support vector machines for palmprint recognition. Procedia Computer Science, vol. 157, pp. 110–117, 2019. DOI: 10.1016/j.procs.2019.08.147.
    [83]
    A. Genovese, V. Piuri, K. N. Plataniotis, F. Scotti. PalmNet: Gabor-PCA convolutional networks for touchless palmprint recognition. IEEE Transactions on Information Forensics and Security, vol. 14, no. 12, pp. 3160–3174, 2019. DOI: 10.1109/TIFS.2019.2911165.
    [84]
    D. X. Zhong, J. S. Zhu. Centralized large margin cosine loss for open-set deep palmprint recognition. IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 6, pp. 1559–1568, 2020. DOI: 10.1109/TCSVT.2019.2904283.
    [85]
    W. M. Matkowski, T. T. Chai, A. W. K. Kong. Palmprint recognition in uncontrolled and uncooperative environment. IEEE Transactions on Information Forensics and Security, vol. 15, pp. 1601–1615, 2020. DOI: 10.1109/TIFS.2019.2945183.
    [86]
    S. P. Zhao, B. Zhang. Deep discriminative representation for generic palmprint recognition. Pattern Recognition, vol. 98, Article number 107071, 2020. DOI: 10.1016/j.patcog.2019.107071.
    [87]
    S. P. Zhao, B. Zhang. Joint constrained least-square regression with deep convolutional feature for palmprint recognition. IEEE Transactions on Systems,Man,and Cybernetics:Systems, pp. 1–12, 2020. DOI: 10.1109/TSMC.2020.3003021.
    [88]
    S. P. Zhao, B. Zhang, C. L. P. Chen. Joint deep convolutional feature representation for hyperspectral palmprint recognition. Information Sciences, vol. 489, pp. 167–181, 2019. DOI: 10.1016/j.ins.2019.03.027.
    [89]
    D. Samai, K. Bensid, A. Meraoumia, A. Taleb-Ahmed, M. Bedda. 2D and 3D palmprint recognition using deep learning method. In Proceedings of the 3rd International Conference on Pattern Analysis and Intelligent Systems, IEEE, Tebessa, Algeria, 2018.
    [90]
    M. Chaa, Z. Akhtar, A. Attia. 3D palmprint recognition using unsupervised convolutional deep learning network and SVM classifier. IET Image Processing, vol. 13, no. 5, pp. 736–745, 2019. DOI: 10.1049/iet-ipr.2018.5642.
    [91]
    N. F. Hassan, H. I. Abdulrazzaq. Pose invariant palm vein identification system using convolutional neural network. Baghdad Science Journal, vol. 15, no. 4, pp. 502–509, 2018. DOI: 10.21123/bsj.15.4.502-509.
    [92]
    S. Lefkovits, L. Lefkovits, L. Szilágyi. Applications of different CNN architectures for palm vein identification. In Proceedings of the 16th International Conference on Modeling Decisions for Artificial Intelligence, Springer, Milan, Italy, vol. 11676, pp. 295–306, 2019.
    [93]
    D. Thapar, G. Jaswal, A. Nigam, V. Kanhangad. PVSNet: Palm vein authentication siamese network trained using triplet loss and adaptive hard mining by learning enforced domain specific features. In Proceedings of the 5th International Conference on Identity, Security, and Behavior Analysis, IEEE, Hyderabad, India, 2019.
    [94]
    S. Chantaf, A. Hilal, R. Elsaleh. Palm vein biometric authentication using convolutional neural networks. In Proceedings of the 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications, Springer, vol. 146, pp. 352–363, 2020.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(30)  / Tables(17)

    Article Metrics

    Article views (322) PDF downloads(100) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return