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Local Binary Patterns: New Variants and Applications pp 245–268Cite as

Histogram-Tensorial Gaussian Representations and its Applications to Facial Analysis

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 506))

Abstract

We present a tensorial framework based in Histograms of Gaussian Binary Maps for facial analysis. The chapter defines and discusses the different steps to build a tensorial representation taking into account the different possible dimensions as orientation, position and scale using the Gaussian derivatives and LBP (Local Binary Patterns). In this chapter, we also consider two different tensorial architectures, the first considers each one of derivative orders as a separate tensor and the second considers the correlation between derivatives when the order is added as supplementary dimension in the final tensor. In addition Multilinear Principal Component Analysis is presented as an algorithm to reduce the dimensions in a tensor without loss of 3-D structure due to vectorization and also as a statistical method for capturing the most discriminative information from each considered dimension in the tensors. Finally, we combine different machine learning methods with our tensorial representation for improving results in Face recognition and Age estimation. In this scope, face recognition is addressed as a classification problem using Kernel Discriminative Vectors to improve recognition rates. On the other hand, Age estimation is addressed as a regression problem using Relevance Vector Machines (RVM).

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Notes

  1. 1.

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

  2. 2.

    http://www.vectoranomaly.com/downloads/downloads.htm

  3. 3.

    The FG-NET ageing database, http://www.fgnet.rsunit.com/.

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Author information

Authors and Affiliations

  1. Center for Machine Vision Research, Department of Computer Science and Engineering, University of Oulu, P.O Box 4500, FI-90014, Oulu, Finland

    John A. Ruiz Hernandez & Matti Pietikäinen

  2. Université de Caen Basse Normandie Campus 2 - U.F.R. Sciences 3 Boulevard Marechal Juin, F-14032, Caen cedex, France

    John A. Ruiz Hernandez

  3. INRIA Grenoble, Rhône-Alpes, 655 Avenue de l’Europe, 38334, Saint Ismier Cedex, France

    James L. Crowley & Augustin Lux

Authors
  1. John A. Ruiz Hernandez
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  2. James L. Crowley
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  3. Augustin Lux
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  4. Matti Pietikäinen
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Corresponding author

Correspondence to John A. Ruiz Hernandez .

Editor information

Editors and Affiliations

  1. Computer Information Systems, Missouri State University, Springfield, USA

    Sheryl Brahnam

  2. Faculty of Education, Science, Technology & Mathematics, University of Canberra, Canberra, Australia

    Lakhmi C. Jain

  3. Departimento di Elettronica e Informatica (DEI), Università di Padova, Padua, Italy

    Loris Nanni

  4. DEIS University of Bologna, Cesena, Italy

    Alessandra Lumini

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Ruiz Hernandez, J.A., Crowley, J.L., Lux, A., Pietikäinen, M. (2014). Histogram-Tensorial Gaussian Representations and its Applications to Facial Analysis. In: Brahnam, S., Jain, L., Nanni, L., Lumini, A. (eds) Local Binary Patterns: New Variants and Applications. Studies in Computational Intelligence, vol 506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39289-4_11

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  • DOI: https://doi.org/10.1007/978-3-642-39289-4_11

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