Greedy search for descriptive spatial face features

Creative Commons License

Gacav C., Benligiray B., Topal C.

2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017, Louisiana, United States Of America, 5 - 09 March 2017, pp.1497-1501 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, sequential forward selection, spatial features
  • Istanbul Technical 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.