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Multi-scale multi-block covariance descriptor with feature selection

  • Abdelmalik MoujahidEmail author
  • Fadi Dornaika
Original Article
  • 10 Downloads

Abstract

This paper investigates a compact face texture representation able to cover the most discriminant features of facial images. The compactness is achieved by the proposed Pyramid Multi-Level (PML) covariance texture descriptor and the feature selection process that is applied on the raw extracted features. In fact, we introduce a framework based mainly on two new aspects. Firstly, we consider an extension of the original covariance descriptor that relies on de-noised covariance matrices obtained using texture descriptors such as local binary pattern and quaternionic local ranking binary pattern images. Secondly, we exploit the resulting covariance descriptor using a PML face representation which allows a multi-level multi-scale feature extraction. Experiments conducted on four public face datasets show the efficacy of the proposed face descriptor and the associated selection schemes.

Keywords

Face texture representation Feature selection Face recognition 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.University of the Basque Country (UPV/EHU)San SebastiánSpain

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