A pose-wise linear illumination manifold model for face recognition using video

Oggie Arandelovic*, Roberto Cipolla

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

39 Citations (Scopus)


The objective of this work is to recognize faces using video sequences both for training and novel input, in a realistic, unconstrained setup in which lighting, pose and user motion pattern have a wide variability and face images are of low resolution. There are three major areas of novelty: (i) illumination generalization is achieved by combining coarse histogram correction with fine illumination manifold-based normalization; (ii) pose robustness is achieved by decomposing each appearance manifold into semantic Gaussian pose clusters, comparing the corresponding clusters and fusing the results using an RBF network; (iii) a fully automatic recognition system based on the proposed method is described and extensively evaluated on 600 head motion video sequences with extreme illumination, pose and motion pattern variation. On this challenging data set our system consistently demonstrated a very high recognition rate (95% on average), significantly outperforming state-of-the-art methods from the literature.

Original languageEnglish
Pages (from-to)113-125
Number of pages13
JournalComputer Vision and Image Understanding
Issue number1
Publication statusPublished - Jan 2009


  • Face recognition
  • Illumination
  • Invariance
  • Manifolds
  • Pose
  • Robustness
  • Video


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