Component-based recognition of facesand facial expressions

Sima Taheri, Vishal M. Patel, Rama Chellappa

Research output: Contribution to journalArticlepeer-review

42 Scopus citations


Most of the existing methods for the recognition of faces and expressions consider either the expression-invariant face recognition problem or the identity-independent facial expression recognition problem. In this paper, we propose joint face and facial expression recognition using a dictionary-based component separation algorithm (DCS). In this approach, the given expressive face is viewed as a superposition of a neutral face component with a facial expression component which is sparse with respect to the whole image. This assumption leads to a dictionary-based component separation algorithm which benefits from the idea of sparsity and morphological diversity. This entails building data-driven dictionaries for neutral and expressive components. The DCS algorithm then uses these dictionaries to decompose an expressive test face into its constituent components. The sparse codes we obtain as a result of this decomposition are then used for joint face and expression recognition. Experiments on publicly available expression and face data sets show the effectiveness of our method.

Original languageEnglish (US)
Article number6662352
Pages (from-to)360-371
Number of pages12
JournalIEEE Transactions on Affective Computing
Issue number4
StatePublished - 2013

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction


  • Simultaneous face and expression recognition
  • dictionary learning
  • expression recognition
  • face recognition
  • image separation
  • sparse representation

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