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Improved Automatic Face Segmentation and Recognition for Applications with Limited Training Data

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Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation (BDAS 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 716))

Abstract

This paper introduces varied pose angle, a new approach to improve face identification given large pose angles and limited training data. Face landmarks are extracted and used to normalize and segment the face. Our approach does not require face frontalization and achieves consistent results. Results are compared using frontal and non-frontal training images for Eigen and Fisher classification of various face pose angles. Fisher scales better with more training samples only with a high quality dataset. Our approach achieves promising results for three well-known face datasets.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Rhodes University, Grahamstown, South Africa

    Dane Brown & Karen Bradshaw

  2. Council for Scientific and Industrial Research, Modelling and Digital Sciences, Pretoria, South Africa

    Dane Brown

Authors
  1. Dane Brown
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  2. Karen Bradshaw
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Corresponding author

Correspondence to Dane Brown .

Editor information

Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Stanisław Kozielski

  2. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Dariusz Mrozek

  3. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Paweł Kasprowski

  4. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Bożena Małysiak-Mrozek

  5. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Daniel Kostrzewa

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Brown, D., Bradshaw, K. (2017). Improved Automatic Face Segmentation and Recognition for Applications with Limited Training Data. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation. BDAS 2017. Communications in Computer and Information Science, vol 716. Springer, Cham. https://doi.org/10.1007/978-3-319-58274-0_33

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  • DOI: https://doi.org/10.1007/978-3-319-58274-0_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58273-3

  • Online ISBN: 978-3-319-58274-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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