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Robust Learning from Normals for 3D Face Recognition

  • Ioannis Marras
  • Stefanos Zafeiriou
  • Georgios Tzimiropoulos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7584)

Abstract

We introduce novel subspace-based methods for learning from the azimuth angle of surface normals for 3D face recognition. We show that the normal azimuth angles combined with Principal Component Analysis (PCA) using a cosine-based distance measure can be used for robust face recognition from facial surfaces. The proposed algorithms are well-suited for all types of 3D facial data including data produced by range cameras (depth images), photometric stereo (PS) and shade-from-X (SfX) algorithms. We demonstrate the robustness of the proposed algorithms both in 3D face reconstruction from synthetically occluded samples, as well as, in face recognition using the FRGC v2 3D face database and the recently collected Photoface database where the proposed method achieves state-of-the-art results. An important aspect of our method is that it can achieve good face recognition/verification performance by using raw 3D scans without any heavy preprocessing (i.e., model fitting, surface smoothing etc.).

Keywords

Face Recognition Azimuth Angle Equal Error Rate Facial Surface Photometric Stereo 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ioannis Marras
    • 1
  • Stefanos Zafeiriou
    • 1
  • Georgios Tzimiropoulos
    • 1
    • 2
  1. 1.Department of ComputingImperial College LondonLondonU.K.
  2. 2.School of Computer ScienceUniversity of LincolnLincolnU.K.

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