Regression Based Non-frontal Face Synthesis for Improved Identity Verification

  • Yongkang Wong
  • Conrad Sanderson
  • Brian C. Lovell
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5702)


We propose a low-complexity face synthesis technique which transforms a 2D frontal view image into views at specific poses, without recourse to computationally expensive 3D analysis or iterative fitting techniques that may fail to converge. The method first divides a given image into multiple overlapping blocks, followed by synthesising a non-frontal representation through applying a multivariate linear regression model on a low-dimensional representation of each block. To demonstrate one application of the proposed technique, we augment a frontal face verification system by incorporating multi-view reference (gallery) images synthesised from the frontal view. Experiments on the pose subset of the FERET database show considerable reductions in error rates, especially for large deviations from the frontal view.


Mean Square Error Discrete Cosine Transform Gaussian Mixture Model Frontal View Equal Error Rate 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yongkang Wong
    • 1
    • 2
  • Conrad Sanderson
    • 1
    • 2
  • Brian C. Lovell
    • 1
    • 2
  1. 1.NICTASt LuciaAustralia
  2. 2.School of ITEEThe University of QueenslandAustralia

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