Three-Dimensional Palmprint Recognition Technology Based on the Fusion of Surface Type and Deep Learning

ZHANG Zonghua; WANG Shengxian; GAO Nan; MENG Zhaozong

doi:10.11999/JEIT200982

Volume 44 Issue 4

Apr. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(4): 1469-1475

ZHANG Zonghua, WANG Shengxian, GAO Nan, MENG Zhaozong. Three-Dimensional Palmprint Recognition Technology Based on the Fusion of Surface Type and Deep Learning[J]. Journal of Electronics & Information Technology, 2022, 44(4): 1469-1475. doi: 10.11999/JEIT200982

Citation:

ZHANG Zonghua, WANG Shengxian, GAO Nan, MENG Zhaozong. Three-Dimensional Palmprint Recognition Technology Based on the Fusion of Surface Type and Deep Learning[J]. Journal of Electronics & Information Technology, 2022, 44(4): 1469-1475. doi: 10.11999/JEIT200982

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Three-Dimensional Palmprint Recognition Technology Based on the Fusion of Surface Type and Deep Learning

doi: 10.11999/JEIT200982

School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China

Funds: The National Natural Science Foundation of China (52075147, 51675160), The National Key R&D Program of China (2017YFF0106404)

Received Date: 2020-11-18
Accepted Date: 2022-02-16
Rev Recd Date: 2022-01-19

Available Online: 2022-02-22

Publish Date: 2022-04-18

Abstract

Abstract

Traditional Two-Dimensional (2D) palmprint recognition is susceptible to the effects of dry humidity, residual image and pressure during image acquisition, which reduces its robustness and accuracy. To solve these problems, Three-Dimensional (3D) palmprint recognition technology is widely studied. The existing 3D palmprint identity authentication technology needs to separate palmprint feature extraction and matching recognition, which not only delays the recognition time, but also increases the difficulty of optimizing the combination of different methods. A 3D palmprint recognition method is proposed based on the fusion of Surface Type (ST) and deep learning. ST images is used to represent 3D palmprint features and to be as input of Convolutional Neural Network (CNN) to realize training. The test image can be automatically extracted the feature information of the palmprint image and complete the identification directly. The experimental results show that the proposed method has an accuracy of 99.43% and a recognition time of 28 ms on the public data set, which has high performance of accuracy and speed compared with the traditional 3D palmprint recognition methods.
- Three-Dimensional(3D) palmprint recognition,
- Surface Type(ST),
- Deep learning,
- Convolutional Neural Networks(CNN)

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References(20)

References

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