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Measuring Spatially- and Directionally-varying Light Scattering from Biological Material
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Face relighting from a single image under arbitrary unknown lighting conditions.

Yang Wang1, Lei Zhang, Zicheng Liu

  • 1Siemens Corporate Research, Princeton, NJ 08540, USA. wangy@cs.cmu.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|September 19, 2009
PubMed
Summary

This study introduces a novel 3D spherical harmonic basis morphable model (SHBMM) for face image manipulation under unknown lighting. The method enhances face recognition robustness, even in extreme lighting and with occlusions.

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Area of Science:

  • Computer Vision
  • Computer Graphics
  • Image Processing

Background:

  • Modifying face image appearance under unknown illumination is challenging with single images.
  • Spherical harmonics approximate Lambertian object appearances under varying light.
  • Morphable models capture facial shape and texture variations.

Purpose of the Study:

  • To develop a 3D spherical harmonic basis morphable model (SHBMM) for face appearance manipulation.
  • To represent faces under arbitrary lighting and pose using low-dimensional SHBMM parameters.
  • To improve robustness to extreme lighting and partial occlusions in face analysis.

Main Methods:

  • Integrated spherical harmonics into a morphable model framework.
  • Proposed a 3D SHBMM with shape, spherical harmonic basis, and illumination parameters.
  • Developed a subregion-based framework using Markov random fields for texture modeling.

Main Results:

  • The SHBMM effectively represents faces under unknown lighting and pose.
  • The subregion-based approach enhances robustness to extreme lighting conditions.
  • Demonstrated improved face recognition rates under challenging lighting scenarios.

Conclusions:

  • The proposed SHBMM framework offers a robust method for face appearance manipulation.
  • The integration of Markov random fields improves resilience to extreme lighting and occlusions.
  • This approach advances face analysis techniques, particularly for recognition tasks.