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Palmprint Recognition Using Siamese Network

  • Dexing Zhong
  • Yuan Yang
  • Xuefeng Du
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10996)

Abstract

Recently, palmprint representation using different descriptors under the incorporation of deep neural networks, always achieves significant recognition performance. In this paper, we proposed a novel method to achieve end-to-end palmprint recognition by using Siamese network. In our network, two parameter-sharing VGG-16 networks were employed to extract two input palmprint images’ convolutional features, and the top network directly obtained the similarity of two input palmprints according to their convolutional features. This method had a good performance on PolyU dataset and achieved a high recognition outcome with an Equal Error Rate (EER) of 0.2819%. To test the robustness of the proposed algorithm, we collected a palmprint dataset called XJTU from the practical daily environment. On XJTU, the EER of our method is 4.559%, which highlighted a promising potential of the usage of palmprint in personal identification system.

Keywords

Palmprint recognition Siamese network Convolutional Neural Networks Feature extraction 

Notes

Acknowledgements

This work is supported by grants from National Natural Science Foundation of China (No. 61105021), Natural Science Foundation of Shaanxi, China (No. 2015JQ6257) and the Fundamental Research Funds for the Central Universities.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Xi’an Jiaotong UniversityXi’anPeople’s Republic of China

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