Multispectral palmprint recognition using Pascal coefficients-based LBP and PHOG descriptors with random sampling View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-02

AUTHORS

Wafa El-Tarhouni, Larbi Boubchir, Mosa Elbendak, Ahmed Bouridane

ABSTRACT

Local binary pattern (LBP) algorithm and its variants have been used extensively to analyse the local textural features of digital images with great success. Numerous extensions of LBP descriptors have been suggested, focusing on improving their robustness to noise and changes in image conditions. In our research, inspired by the concepts of LBP feature descriptors and a random sampling subspace, we propose an ensemble learning framework, using a variant of LBP constructed from Pascal’s coefficients of n-order and referred to as a multiscale local binary pattern. To address the inherent overfitting problem of linear discriminant analysis, PCA was applied to the training samples. Random sampling was used to generate multiple feature subsets. In addition, in this work, we propose a new feature extraction technique that combines the pyramid histogram of oriented gradients and LBP, where the features are concatenated for use in the classification. Its performance in recognition was evaluated using the Hong Kong Polytechnic University database. Extensive experiments unmistakably show the superiority of the proposed approach compared to state-of-the-art techniques. More... »

PAGES

593-603

References to SciGraph publications

  • 2007. Multi-scale Local Binary Pattern Histograms for Face Recognition in ADVANCES IN BIOMETRICS
  • 2013-10. Bidirectional representation for face recognition across pose in NEURAL COMPUTING AND APPLICATIONS
  • 2013-12. Evaluation of noise robustness for local binary pattern descriptors in texture classification in EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING
  • 2009. Research of Palmprint Recognition Based on 2DPCA in ADVANCES IN NEURAL NETWORKS – ISNN 2009
  • 2006-10. Random Sampling for Subspace Face Recognition in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00521-017-3092-7

    DOI

    http://dx.doi.org/10.1007/s00521-017-3092-7

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1091416258


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