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Face Recognition across Pose using ELM Framework

Author

Listed:
  • Kumud Arora
  • Poonam Garg
  • Virendra P. Vishwakarma

Abstract

This paper proposes the use of Extreme Learning Machine regression and classification framework to recognize face across pose. ELM regression framework is used to generate virtual frontal view from its corresponding side view. Kernel version of ELM is used for the non-linear mapping estimation between frontal and its corresponding non frontal view. Non Linear estimation using proposed KELM is found to be satisfactory even in the case of reduced image coefficients. In addition to regression framework, this paper also explored few popular face features like LBP, HOG, appearance and entropy features with ELM classification framework. Combination of appearance and entropy feature set is found to give best classification accuracy among the considered feature set. Proposed experiments are conducted on subset of FERET database with images up to 45° pose variation.

Suggested Citation

  • Kumud Arora & Poonam Garg & Virendra P. Vishwakarma, 2018. "Face Recognition across Pose using ELM Framework," European Journal of Electrical Engineering and Computer Science, European Open Science, vol. 2(3), April.
    • Handle: RePEc:epw:ejece0:v:2:y:2018:i:3:id:19023
    DOI: 10.24018/ejece.2018.2.3.23
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    References listed on IDEAS

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    1. Senyue Zhang & Wenan Tan, 2016. "An Extreme Learning Machine Based on the Mixed Kernel Function of Triangular Kernel and Generalized Hermite Dirichlet Kernel," Discrete Dynamics in Nature and Society, John Wiley & Sons, vol. 2016(1).
    2. Senyue Zhang & Wenan Tan, 2016. "An Extreme Learning Machine Based on the Mixed Kernel Function of Triangular Kernel and Generalized Hermite Dirichlet Kernel," Discrete Dynamics in Nature and Society, Hindawi, vol. 2016, pages 1-11, October.
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