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Local Binary LDA for Face Recognition

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Biometrics and ID Management (BioID 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6583))

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Abstract

Extracting discriminatory features from images is a crucial task for biometric recognition. For this reason, we have developed a new method for the extraction of features from images that we have called local binary linear discriminant analysis (LBLDA), which combines the good characteristics of both LDA and local feature extraction methods. We demonstrated that binarizing the feature vector obtained by LBLDA significantly improves the recognition accuracy. The experimental results demonstrate the feasibility of the method for face recognition as follows: on XM2VTS face image database, a recognition accuracy of 96.44% is obtained using LBLDA, which is an improvement over LDA (94.41%). LBLDA can also outperform LDA in terms of computation speed.

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Author information

Authors and Affiliations

  1. Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000, Zagreb, Croatia

    Ivan Fratric & Slobodan Ribaric

Authors
  1. Ivan Fratric
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  2. Slobodan Ribaric
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Editor information

Editors and Affiliations

  1. Bradenburg University of Applied Sciences, 14737, Brandenburg an der Havel, Germany

    Claus Vielhauer

  2. Faculty of Computer Science, Otto-von-Guericke-University Magdeburg, 39016, Magdeburg, Germany

    Jana Dittmann

  3. Swiss Federal Institute of Technology Lausanne (EPFL), 1015, Lausanne, Switzerland

    Andrzej Drygajlo

  4. Roskilde University, 4000, Roskilde, Denmark

    Niels Christian Juul

  5. Department of Electronics, University of Kent, CT2 7NT, Canterbury, Kent, UK

    Michael C. Fairhurst

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© 2011 Springer-Verlag Berlin Heidelberg

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Fratric, I., Ribaric, S. (2011). Local Binary LDA for Face Recognition. In: Vielhauer, C., Dittmann, J., Drygajlo, A., Juul, N.C., Fairhurst, M.C. (eds) Biometrics and ID Management. BioID 2011. Lecture Notes in Computer Science, vol 6583. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19530-3_14

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  • DOI: https://doi.org/10.1007/978-3-642-19530-3_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-19529-7

  • Online ISBN: 978-3-642-19530-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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