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Face Recognition and Pose Estimation with Parametric Linear Subspaces

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Book cover Applied Pattern Recognition

Part of the book series: Studies in Computational Intelligence ((SCI,volume 91))

We present a general statistical framework for modeling and processing head pose information in 2D grayscale images: analyzing, synthesizing, and identifying facial images with arbitrary 3D head poses. The proposed framework offers a compact view-based data-driven model which provides bidirectional mappings between facial views and their corresponding parameters of 3D head angle. Such a mapping-based model implicitly captures 3D geometric nature of the problem without explicitly reconstructing a 3D structural model from data. The proposed model consists of a hierarchy of local linear models that cover a range of parameters by piecing together a set of localized models. This piecewise design allows us to accurately cover a wide parameter range, while the linear design, using efficient principal component analysis and singular value decomposition algorithms, facilitates generalizability to unfamiliar cases by avoiding overfitting. We apply the model to realize robust pose estimation using the view-to-pose mapping and pose-invariant face recognition using the proposed model to represent a known face. Quantitative experiments are conducted using a database of Cyberware-scanned 3D face models. The results demonstrate high accuracy for pose estimation and high recognition rate for previously unseen individuals and views for a wide range of 3D head rotation.

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Author information

Authors and Affiliations

  1. Department of Computer Science, San Francisco State University, 94132-4163, San Francisco, CA, USA

    Kazunori Okada

  2. Frankfurt Institute of Advanced Studies, Science Campus Riedberg, 60438, Frankfurt, Germany

    Christoph von der Malsburg

Authors
  1. Kazunori Okada
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  2. Christoph von der Malsburg
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Editor information

Editors and Affiliations

  1. Institute of Computer Science and Applied Mathematics, Neubrückstrasse 10, CH-3012, Bern, Switzerland

    Horst Bunke

  2. University of South Florida, 4202 E. Fowler Ave, 33620, Tampa, FL, USA

    Abraham Kandel

  3. Ben-Gurion University of the Negev, 84105, Beer-Sheva, Israel

    Mark Last

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Okada, K., von der Malsburg, C. (2008). Face Recognition and Pose Estimation with Parametric Linear Subspaces. In: Bunke, H., Kandel, A., Last, M. (eds) Applied Pattern Recognition. Studies in Computational Intelligence, vol 91. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76831-9_3

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  • DOI: https://doi.org/10.1007/978-3-540-76831-9_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-76830-2

  • Online ISBN: 978-3-540-76831-9

  • eBook Packages: EngineeringEngineering (R0)

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