Image-based face illumination transferring using logarithmic total variation models

  • Authors:

  • Qing Li;Wotao Yin;Zhigang Deng

  • Affiliations:

  • University of Houston, Computer Graphics and Interactive Media Lab, Department of Computer Science, Houston, TX, USA;Rice University, Department of Computational and Applied Mathematics, Houston, TX, USA;University of Houston, Computer Graphics and Interactive Media Lab, Department of Computer Science, Houston, TX, USA

  • Venue:
  • The Visual Computer: International Journal of Computer Graphics
  • Year:
  • 2009

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Abstract

In this paper, we present a novel image-based technique that transfers illumination from a source face image to a target face image based on the Logarithmic Total Variation (LTV) model. Our method does not require any prior information regarding the lighting conditions or the 3D geometries of the underlying faces. We first use a Radial Basis Functions (RBFs)-based deformation technique to align key facial features of the reference 2D face with those of the target face. Then, we employ the LTV model to factorize each of the two aligned face images to an illumination-dependent component and an illumination-invariant component. Finally, illumination transferring is achieved by replacing the illumination-dependent component of the target face by that of the reference face. We tested our technique on numerous grayscale and color face images from various face datasets including the Yale face Database, as well as the application of illumination-preserved face coloring.