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Gacav C., Benligiray B., Topal C.

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Louisiana, United States Of America, 5 - 09 March 2017, pp.1497-1501 identifier identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/icassp.2017.7952406
  • City: Louisiana
  • Country: United States Of America
  • Page Numbers: pp.1497-1501
  • Keywords: facial expression recognition, spatial features, sequential forward selection
  • Anadolu University Affiliated: Yes


Facial expression recognition methods use a combination of geometric and appearance-based features. Spatial features are derived from displacements of facial landmarks, and carry geometric information. These features are either selected based on prior knowledge, or dimension-reduced from a large pool. In this study, we produce a large number of potential spatial features using two combinations of facial landmarks. Among these, we search for a descriptive subset of features using sequential forward selection. The chosen feature subset is used to classify facial expressions in the extended Cohn-Kanade dataset (CK+), and delivered 88.7% recognition accuracy without using any appearance-based features.