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Finger Vein Recognition Based on Weighted Graph Structural Feature Encoding

  • Shuyi Li
  • Haigang Zhang
  • Guimin Jia
  • Jinfeng Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10996)

Abstract

The finger-vein recognition performance is usually sensitive to illumination and pose variation. Exploring suitable feature representation method is therefore significant for finger-vein recognition improvement. In this paper, we propose a novel feature encoding method based on local graph structure (LGS), which behaves better in improving the matching accuracy of features. In terms of the variations of veins in running direction, oriented Gabor filters are firstly used for venous region enhancement. Then, a symmetric cross-weighted local graph structure (SCW-LGS) is proposed to locally represent the gradient relationships among the pixels in a neighborhood of the Gabor enhanced images. Based on SCW-LGS, a multi-orientation feature encoding method is developed for vein network feature representation. Experimental results show that the proposed approach achieves better performance than the state-of-the-art approaches on finger-vein recognition.

Keywords

Feature encoding Finger-vein recognition Local graph structure Gabor filter 

Notes

Acknowledgements

This work is supported by National Natural Science Foundation of China (No. 61502498, No. 61379102, NO. U1433120) and the Fundamental Research Funds for the Central Universities (NO. 3122017001).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Shuyi Li
    • 1
  • Haigang Zhang
    • 1
  • Guimin Jia
    • 1
  • Jinfeng Yang
    • 1
  1. 1.Tianjin Key Lab for Advanced Signal ProcessingCivil Aviation University of ChinaTianjinChina

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