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Data and Information Dimensionality in Non-cooperative Face Recognition

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Signal and Image Processing for Biometrics

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 292))

Abstract

Data, information dimensionality and manifold learning techniques are related issues that are gaining prominence in biometrics. Problems dealing with large amounts of data often have dimensionality issues, leading to uncertainty and inefficiency. This chapter presents concepts of manifold learning and information geometry, and discusses how the manifold geometry can be exploited to obtain biometric data representations in lower dimensions. It is also explained how biometric data that are modeled with suitable probability distributions, can be classified accurately using geodesic distances on probabilistic manifolds, or approximations when the analytic geodesic distance solutions are not known. Also, we discuss some of the representative manifold based methods applied to face recognition, and point out future research directions.

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Notes

  1. 1.

    It should be noted that this is different from an actual embedding of the manifold in Euclidean space, see also [28].

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Author information

Authors and Affiliations

  1. Ghent University, Ghent, Belgium

    Geert Verdoolaege

  2. Laboratory for Plasma Physics, Royal Military Academy, Brussels, Belgium

    Geert Verdoolaege

  3. Instituto de Informática, UFRGS, Porto Alegre, Brazil

    John Soldera

  4. Guardian Tecnologia da Informação, Caxias do Sul, Brazil

    Thiarlei Macedo

  5. Instituto de Informática and Programa de Pós-Graduação em Engenharia Elétrica, UFRGS, Porto Alegre, Brazil

    Jacob Scharcanski

Authors
  1. Geert Verdoolaege
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  2. John Soldera
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  3. Thiarlei Macedo
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  4. Jacob Scharcanski
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Corresponding author

Correspondence to Jacob Scharcanski .

Editor information

Editors and Affiliations

  1. Federal University of Rio Grande do Sul, Porto Alegre, Brazil

    Jacob Scharcanski

  2. IT-Instituto de Telecomunicações, University of Beira Interior, Covilhã, Portugal

    Hugo Proença

  3. Indiana University-Purdue University Indianapolis, Indianapolis, Indiana, USA

    Eliza Du

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Verdoolaege, G., Soldera, J., Macedo, T., Scharcanski, J. (2014). Data and Information Dimensionality in Non-cooperative Face Recognition. In: Scharcanski, J., Proença, H., Du, E. (eds) Signal and Image Processing for Biometrics. Lecture Notes in Electrical Engineering, vol 292. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54080-6_1

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  • DOI: https://doi.org/10.1007/978-3-642-54080-6_1

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