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A Novel Hybrid Approach for 3D Face Recognition Based on Higher Order Tensor

  • Mohcene Bessaoudi
  • Mebarka Belahcene
  • Abdelmalik Ouamane
  • Ammar Chouchane
  • Salah Bourennane
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 50)

Abstract

This paper presents a new hybrid approach for 3D face verification based on tensor representation in the presence of illuminations, expressions and occlusion variations. Depth face images are divided into sub-region and the Multi-Scale Local Binarised Statistical Image Features (MSBSIF) histogram are extracted from each sub-region and arranged as a third order tensor. Furthermore, to reduce the dimensionality of this tensor data, we use a novel hybrid approach based on two steps of dimensionality reduction multilinear and non-linear. Firstly, Multilinear Principal Component Analysis (MPCA) is used. MPCA projects the input tensor in a new lower subspace in which the dimension of each tensor mode is reduced. After that, the non-linear Exponential Discriminant Analysis (EDA) is used to discriminate the faces of different persons. Finally, the matching is performed using distance measurement. The proposed approach (MPCA+EDA) has been tested on the challenging face database Bosporus 3D and the experimental results show that our method achieves a high verification performance compared with the state of the art.

Keywords

3D face verification Depth image Tensor representation Histograms local features 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mohcene Bessaoudi
    • 1
  • Mebarka Belahcene
    • 1
  • Abdelmalik Ouamane
    • 1
  • Ammar Chouchane
    • 1
  • Salah Bourennane
    • 2
  1. 1.LI3C LaboratoryUniversity of BiskraBiskraAlgeria
  2. 2.Institut FresnelUniversité de MarseilleMarseilleFrance

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