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Local Descriptor and Feature Selection Based Palmprint Recognition System

  • Chérif TaoucheEmail author
  • Hacene Belhadef
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1073)

Abstract

In this paper, we present a system based on a feature selection approach for solving the recognition problem in a multimodal biometric system combining both left and right palmprints of the same subject. In particular, the fusion of two or more traits, at feature-level, results in a long feature vector that needs large storage space, makes the execution time of the recognition task very long, and may include redundant and irrelevant features that can affect the recognition accuracy. To overcome these problems, feature selection is performed using genetic algorithms (GAs) and backtracking search algorithm for a comparison purpose. The experimental results show the usefulness of feature selection, especially the use of genetic algorithms, on the robustness of the multimodal biometric system as regards the feature vector length and run-time reduction, and the significant increase of the recognition rate.

Keywords

Multimodal biometrics Feature selection Local descriptor Palmprint recognition Feature-level fusion Evolutionary algorithms 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.RELA(CS)² LaboratoryUniversity of Oum El-BouaghiOum El-BouaghiAlgeria
  2. 2.NTIC FacultyUniversity of Constantine 2ConstantineAlgeria

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