3D face discriminant analysis using gauss-markov posterior marginals

Omar Ocegueda, Tianhong Fang, Shishir K. Shah, Ioannis A. Kakadiaris

Research output: Contribution to journalArticlepeer-review

43 Scopus citations


We present a Markov Random Field model for the analysis of lattices (e.g., images or 3D meshes) in terms of the discriminative information of their vertices. The proposed method provides a measure field that estimates the probability of each vertex being discriminative or nondiscriminative for a given classification task. To illustrate the applicability and generality of our framework, we use the estimated probabilities as feature scoring to define compact signatures for three different classification tasks: 1) 3D Face Recognition, 2) 3D Facial Expression Recognition, and 3) Ethnicity-based Subject Retrieval, obtaining very competitive results. The main contribution of this work lies in the development of a novel framework for feature selection in scenaria in which the most discriminative information is smoothly distributed along a lattice.

Original languageEnglish (US)
Article number6205766
Pages (from-to)728-739
Number of pages12
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number3
StatePublished - 2013


  • Feature evaluation and selection
  • Markov random fields
  • face and gesture recognition
  • image processing and computer vision
  • object recognition
  • pattern recognition
  • segmentation

ASJC Scopus subject areas

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


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