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A Class Specific Representation Learning for Illumination Tolerant Face Recognition

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Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1036))

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Abstract

An approach of class specific representation based learning for illumination tolerant face recognition is reported in this paper. Autoencoder based representation and class specific reconstruction along with phase correlation in frequency domain for classification is proposed. Autoencoder based representation is evaluated as very few number of training images are sufficient to handle the entire variation of test face subspace. Phase correlation is used at the classification stage to handle the illumination problem as intensity is the primary concern. This judicial combination of representation and classification shows improved recognition accuracy on benchmark databases. The performance of the proposed approach compared to another state-of-the-art technique on other representation based learning is established with extensive experimental. Advantage of the proposed approach is also shown by the performance analysis with single training image, which is necessary for some real time applications.

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Notes

  1. 1.

    http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html.

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Authors and Affiliations

  1. Department of Electrical Engineering, Future Institute of Engineering and Management, Kolkata, India

    Tiash Ghosh & Pradipta K. Banerjee

Authors
  1. Tiash Ghosh
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  2. Pradipta K. Banerjee
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Corresponding authors

Correspondence to Tiash Ghosh or Pradipta K. Banerjee .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of South Dakota, Vermillion, SD, USA

    K. C. Santosh

  2. Solapur University, Solapur, India

    Ravindra S. Hegadi

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Ghosh, T., Banerjee, P.K. (2019). A Class Specific Representation Learning for Illumination Tolerant Face Recognition. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1036. Springer, Singapore. https://doi.org/10.1007/978-981-13-9184-2_47

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  • DOI: https://doi.org/10.1007/978-981-13-9184-2_47

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9183-5

  • Online ISBN: 978-981-13-9184-2

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