Total variation models for variable lighting face recognition

Terrence Chen, Wotao Yin, Xiang Sean Zhou, Dorin Comaniciu, Thomas S. Huang

Research output: Contribution to journalArticlepeer-review


In this paper, we present the logarithmic total variation (LTV) model for face recognition under varying illumination, including natural lighting conditions, where we rarely know the strength, direction, or number of light sources. The proposed LTV model has the ability to factorize a single face image and obtain the illumination invariant facial structure, which is then used for face recognition. Our model is inspired by the SQI model but has better edge-preserving ability and simpler parameter selection. The merit of this model is that neither does it require any lighting assumption nor does it need any training. The LTV model reaches very high recognition rates in the tests using both Yale and CMU PIE face databases as well as a face database containing 765 subjects under outdoor lighting conditions.

Original languageEnglish (US)
Pages (from-to)1519-1524
Number of pages6
JournalIEEE transactions on pattern analysis and machine intelligence
Issue number9
StatePublished - Sep 2006
Externally publishedYes


  • Face and gesture recognition
  • Image processing and computer vision
  • Pattern analysis
  • Signal processing

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


Dive into the research topics of 'Total variation models for variable lighting face recognition'. Together they form a unique fingerprint.

Cite this