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Local Binary Pattern Based ELM for Face Identification

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Proceedings of International Conference on Artificial Intelligence and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1164))

Abstract

In this chapter, local binary pattern (LBP) based extreme learning machine (ELM) is presented for identification of high-dimensional face images of different resolution. In this scheme, LBP is used for the representation of micro-regions of face image in the form of feature vectors which are further concatenated to be utilized as face descriptors for representation of face image. The obtained face descriptors are classified using ELM which is a simple and fast learning algorithm. Extensive simulations on AT&T and Yale databases of face images illustrate the effectiveness of the proposed scheme.

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Authors and Affiliations

  1. University School of Information Communication Technology, Guru Gobind Singh Indraprastha University, New Delhi, 110078, India

    Bhawna Ahuja & Virendra P. Vishwakarma

Authors
  1. Bhawna Ahuja
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  2. Virendra P. Vishwakarma
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Corresponding authors

Correspondence to Bhawna Ahuja or Virendra P. Vishwakarma .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, New Delhi, India

    Poonam Bansal

  2. Department of Electrical and Electronics Engineering, Maharaja Surajmal Institute of Technology, New Delhi, India

    Meena Tushir

  3. Department of Automation and Applied Software, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  4. Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, India

    Rajeev Srivastava

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Ahuja, B., Vishwakarma, V.P. (2021). Local Binary Pattern Based ELM for Face Identification. In: Bansal, P., Tushir, M., Balas, V., Srivastava, R. (eds) Proceedings of International Conference on Artificial Intelligence and Applications. Advances in Intelligent Systems and Computing, vol 1164. Springer, Singapore. https://doi.org/10.1007/978-981-15-4992-2_34

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