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Veinseg-Net: A Novel Finger Vein Segmentation Algorithm Based on Deep Learning

Published: 31 May 2023 Publication History

Abstract

On the basis of the original U-Net and ResNet finger vein segmentation methods, this paper proposes a new finger vein segmentation method based on the combination of the above two methods. This method completes feature extraction by using depth residual network instead of U-Net. Using extended convolution instead of traditional convolution solves the risk of loss of finger vein information extracted by pooling layer, so that the method can extract finger vein veins more accurately without pooling. The algorithm uses the Mish constructor to replace the ReLU constructor to strengthen the anti-compression ability of the model and make vein image more continuous. From the comparison of experimental results, it is not difficult to see that the segmentation accuracy of the newly proposed Veinseg-Net network on multiple finger vein databases are better than those of other depth learning segmentation methods. The good segmentation accuracy of finger vein image can help the finger vein recognition process in the subsequent environment intelligence.

References

[1]
Kono M, 2000, A new method for the identification of individuals by using of vein pattern matching of a finger. //Proc. Fifth Symposium on Pattern Measurement, Yamaguchi, Japan, pp 9-12.
[2]
Qiao W Y, 2015, Technical research based on internal characteristics of finger vein. Harbin Engineering University.
[3]
Li S Y, 2014, Research on Key Problems of Finger Vein Recognition. Northeastern University.
[4]
Yuan W Q, 2016, Finger vein image segmentation method based on local gray minimum. Computer Technology and Development. 26 (6):109-111.
[5]
Yuan W Q, 2011, Palm-vein image segmentation method based on local gray minimum. Journal of Optoelectronics. Laser, vol. 7.
[6]
Wang J, Xiao J, Lin W, 2015, Discriminative and generative vocabulary tree: With application to vein image authentication and recognition. Image and Vision Computing. 34: 51-62.
[7]
Matkowski W M, Chan F K S, Kong A W K, 2019, A study on wrist identification for forensic investigation. Image and Vision Computing. 88: 96-112.
[8]
Liu X, 2019, Research of finger-vein feature extraction and anti-spoofing detection based on deep learning. Chongqing Technology and Business University.
[9]
Ronneberger O, Fischer P, and Brox T, 2015, U-Net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention. Springer, pp 234-241.
[10]
Guo C, Szemenyei M, Yi Y, Wang W, Chen B, and Fan C, 2021, SA-UNet: Spatial attention U-Net for retinal vessel segmentation. In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, pp 1236-1242.
[11]
Badrinarayanan V, Kendall A, and Cipolla R, 2017, SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence, vol. 39(12):2481-2495.
[12]
Huang H, Lin L, Tong R, Hu H, Zhang Q, Iwamoto Y, Han X, Chen Y W, and Wu J, 2020, UNet 3+: A full-scale connected Unet for medical image segmentation. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp 1055-1059.
[13]
Chen C, Liu X, Ding M, Zheng J, and Li J, 2019, 3d dilated multi-fiber network for real-time brain tumor segmentation in MRI. In International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, pp 184-192.
[14]
Brügger R, Baumgartner C F, and Konukoglu E, 2019, A partially reversible u-net for memory-efficient volumetric image segmentation. In International conference on medical image computing and computer-assisted intervention. Springer, pp 429-437.
[15]
Milletari F, Navab N, and Ahmadi S A, 2016, V-Net: Fully convolutional neural networks for volumetric medical image segmentation. In 2016 fourth international conference on 3D vision (3DV). IEEE, pp. 565-571.
[16]
Jégou S, Drozdzal M, Vazquez D, Romero A, and Bengio Y, 2017, The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 11-19.
[17]
Chen L C, Papandreou G, Kokkinos I, Murphy K, and Yuille A L, 2017, Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence, 40(4):834-848.
[18]
Chen L C, Papandreou G, Schroff F, and Adam H, 2017, Rethinking atrous convolution for semantic image segmentation. ArXiv preprint arXiv:1706.05587.
[19]
Chen L C, Zhu Y, Papandreou G, Schroff F, and Adam H, 2017, Encoder-decoder with atrous separable convolution for semantic image segmentation. In Proceedings of the European conference on computer vision (ECCV), pp 801-818.
[20]
Xu H, Ye C, Zhang F, Li X, and Zhang C, 2020, A medical image segmentation method with anti-noise and bias-field correction. IEEE Access, vol. 8, pp 98548-98561.
[21]
Zhang Z and Song J, 2019, An adaptive fuzzy level set model with local spatial information for medical image segmentation and bias correction. IEEE Access, vol. 7, pp 27322-27338.
[22]
Shi H, Wang M, and Wang C, 2020, Pattern-based autonomous smooth switching control for constrained flexible joint manipulator. Neurocomputing 492:162-173.
[23]
Wu D, He Y, Luo X, 2021, A latent factor analysis-based approach to online sparse streaming feature selection. IEEE Transactions on Systems, Man, and Cybernetics: Systems.
[24]
Wu D, Luo X, Shang M, 2020, A data-characteristic-aware latent factor model for web services QoS prediction. IEEE Transactions on Knowledge and Data Engineering.
[25]
He K, Zhang X, Ren S, and Sun J, 2016, Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770-778.
[26]
Mish M D, 2019, A self regularized non-monotonic neural activation function. ArXiv preprint arXiv:1908.08681, 4(2).
[27]
Xu X, 2019, Research on iris localization based on deep convolution network. Journal of Integration Technology, 5(1):57-67.
[28]
Chen J, Lu Y, Yu Q, 2021, TransUNet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306.
[29]
Malaysian datasets, 2014: http://drfendi.com/fv_usm_database/
[30]
Shandong University datasets, 2011: http://mla.sdu.edu.cn/sdumla-hmt.html

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BIC '23: Proceedings of the 2023 3rd International Conference on Bioinformatics and Intelligent Computing
February 2023
398 pages
ISBN:9798400700200
DOI:10.1145/3592686
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 31 May 2023

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