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BoVW Based Feature Selection for Uyghur Offline Signature Verification

  • Shu-Jing Zhang
  • Mahpirat
  • Yunus Aysa
  • Kurban Ubul
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10996)

Abstract

As an important research direction in the field of biometrics, offline signature verification plays an important role. This paper proposes BoVW based on feature selection algorithm MRMR for offline signature verification. In this paper, eigenvectors were formed by extracting visual word features and the features were obtained by building a visual word bag of signature samples. In order to improve the relevance between eigenvectors and categories, and reduce the redundancy between features, the Maximum Relevance and Minimum Redundancy algorithm was used to select features of visual word eigenvectors. The algorithm can find the optimal feature subset. The experiments were conducted using 1200 samples from in our Uyghur signature database, and comparison experiments were carried on selecting 2640 samples from CEDAR database. It was obtained 93.81% of ORR from Uyghur signature and 95.38% of ORR using Latin signature from CEADER database respectively. The experimental results indicated the efficiency of proposed method in this paper.

Keywords

Offline signature verification BoVW MRMR Feature selection 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 61363064, 61563052, 61163028).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Shu-Jing Zhang
    • 1
  • Mahpirat
    • 2
  • Yunus Aysa
    • 1
  • Kurban Ubul
    • 1
  1. 1.School of Information Science and EngineeringXinjiang UniversityUrumqiChina
  2. 2.Academic Affairs DivisionXinjiang UniversityUrumqiChina

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