摘要(英) |
In an age when people are more and more concerned about personal privacy protection,
all parties are working hard to conceive new ways of identity authentication to ensure that the
user′s identity can be verified and will not be stolen. Therefore, this paper investigates the
application of deep learning neural networks to multi-modal biometric recognition of multispectral palm images in order to improve the accuracy of body recognition. In this paper, we
use the CASIA multi-spectral palm image dataset as the basis of the experiment. Each palm
image of each spectrum contains multi-modal biometric features such as palm print, hand shape,
knuckle pattern, and fingerprint, etc. The multi-modal biometric features of the palm are input
into the deep learning network, and each biometric feature of the palm image is applied to
improve the accuracy of recognition.
In order to effectively improve the accuracy of the trained models, we first tried to train and
test the unpre-trained network models and found that the results were not as good as expected;
we introduced the pre-trained models as a way to improve the accuracy of recognition, and the
accuracy obtained after training increased significantly. In the experiments, data augmentation
was performed using common camera environment changes such as random rotation, panning,
and luminance changes, and it was found that this not only enhanced the accuracy of the model,
but also enhanced the ability of the model to adapt to image changes. The experimental results
validate the potential of using multispectral palm multimodal features for body recognition by
using appropriate pre-training deep learning neural network models and increasing the amount
of data enhancement. |
參考文獻 |
[1] ImageNet :http://image-net.org/
[2] Khan, Zohaib, et al. "Multispectral palmprint encoding and recognition." arXiv preprint arXiv:1402.2941 (2014).
[3] Biometrics Ideal Test http://biometrics.idealtest.org/index.jsp
[4] CASIA Multi-Spectral Palmprint Database http://biometrics.idealtest.org/dbDetailForUser.do?id=6
[5] Weiss, Karl, Taghi M. Khoshgoftaar, and DingDing Wang. "A survey of transfer learning." Journal of Big data 3.1 (2016): 1-40.
[6] Yosinski, Jason, et al. "How transferable are features in deep neural networks?." arXiv preprint arXiv:1411.1792 (2014).
[7] He, Kaiming, Ross Girshick, and Piotr Dollár. "Rethinking imagenet pre-training." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2019.
[8] Kolesnikov, Alexander, et al. "Big transfer (bit): General visual representation learning." arXiv preprint arXiv:1912.11370 6.2 (2019): 8.
[9] LeCun, Yann, et al. "Gradient-based learning applied to document recognition." Proceedings of the IEEE 86.11 (1998): 2278-2324.
[10] Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems 25 (2012): 1097-1105.
[11] Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).
[12] Szegedy, Christian, et al. "Going deeper with convolutions." Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
[13] Sifre, L., and S. Mallat. "Rigid-Motion Scattering for Image Classification. arXiv 2014." arXiv preprint arXiv:1403.1687.
[14] Howard, Andrew G., et al. "Mobilenets: Efficient convolutional neural networks for mobile vision applications." arXiv preprint arXiv:1704.04861 (2017).
[15] Chollet, François. "Xception: Deep learning with depthwise separable convolutions." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
[16] He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
[17] He, Kaiming, et al. "Identity mappings in deep residual networks." European conference on computer vision. Springer, Cham, 2016.
[18] Huang, Gao, et al. "Densely connected convolutional networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
[19] Wong, Sebastien C., et al. "Understanding data augmentation for classification: when to warp?." 2016 international conference on digital image computing: techniques and applications (DICTA). IEEE, 2016.
[20] Srivastava, Nitish, et al. "Dropout: a simple way to prevent neural networks from overfitting." The journal of machine learning research 15.1 (2014): 1929-1958.
[21] Kingma, Diederik P., and Jimmy Ba. "Adam: A method for stochastic optimization." arXiv preprint arXiv:1412.6980 (2014).
[22] Janocha, Katarzyna, and Wojciech Marian Czarnecki. "On loss functions for deep neural networks in classification." arXiv preprint arXiv:1702.05659 (2017).
[23] Gong, Weiyong, et al. "Palmprint recognition based on convolutional neural network-Alexnet." 2019 Federated Conference on Computer Science and Information Systems (FedCSIS). IEEE, 2019.
[24] Dong, Xueqiu, Liye Mei, and Junhua Zhang. "Palmprint Recognition Based on Deep Convolutional Neural Networks." International Conference on Computer Science and Intelligent Communication. 2018.
[25] Sun, Z., et al. "Ordinal palmprint representation for personal identification. 2005." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
[26] Wu, Xiangqian, Kuanquan Wang, and David Zhang. "Palmprint texture analysis using derivative of Gaussian filters." 2006 International Conference on Computational Intelligence and Security. Vol. 1. IEEE, 2006.
[27] Kong, AW-K., and David Zhang. "Competitive coding scheme for palmprint verification." Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.. Vol. 1. IEEE, 2004.
[28] Raghavendra, Ramachandra, and Christoph Busch. "Novel image fusion scheme based on dependency measure for robust multispectral palmprint recognition." Pattern recognition 47.6 (2014): 2205-2221.
[29] Thamri, Essia, Kamel Aloui, and Mohamed Saber Naceur. "New approach to extract palmprint lines." 2018 International Conference on Advanced Systems and Electric Technologies (IC_ASET). IEEE, 2018. |