An information-theoretic approach to face recognition from face motion manifolds

Oggie Arandelovic*, Roberto Cipolla

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)


In this work, we consider face recognition from face motion manifolds (FMMs). The use of the resistor-average distance (RAD) as a dissimilarity measure between densities confined to FMMs is motivated in the proposed information-theoretic approach to modelling face appearance. We introduce a kernel-based algorithm that makes use of the simplicity of the closed-form expression for RAD between two Gaussian densities, while allowing for modelling of complex and nonlinear, but intrinsically low-dimensional manifolds. Additionally, it is shown how geodesically local FMM structure can be modelled, naturally leading to a stochastic algorithm for generalizing to unseen modes of data variation. Recognition performance of our method is demonstrated experimentally and is shown to exceed that of state-of-the-art algorithms. Recognition rate of 98% was achieved on a database of 100 people under varying illumination.

Original languageEnglish
Pages (from-to)639-647
Number of pages9
JournalImage and Vision Computing
Issue number6
Publication statusPublished - 1 Jun 2006


  • Face motion manifolds
  • Face recognition
  • Kernel
  • Resistor-average distance


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