Sequential forward feature selection for facial expression recognition Yüz Ifadesi Tanima Için Ardisik Ileri Öznitelik Seçimi

Gacav C., Benligiray B., Topal C.

24th Signal Processing and Communication Application Conference, SIU 2016, Zonguldak, Turkey, 16 - 19 May 2016, pp.1481-1484 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/siu.2016.7496031
  • City: Zonguldak
  • Country: Turkey
  • Page Numbers: pp.1481-1484
  • Keywords: Cohn-Kanade dataset, facial expression recognition, feature selection, forward sequential feature selection, support vector machines
  • Istanbul Technical University Affiliated: Yes


Facial expression recognition is an important computer vision problem with various applications. In this study, we investigate the effectiveness of features derived from facial landmarks in facial expression recognition. Distances between two combinations of facial landmarks constitute a distance vector. Features we use are the changes in the distance vectors extracted from expressive and neutral states of the face. The obtained feature vector contains elements that are relatively useless in expression recognition. By applying forward sequential feature selection, a subset of the most effective elements is formed. The chosen features are classified using a multi-class support vector machine. The performance of the proposed method is measured using Extended Cohn-Kanade dataset with seven expressions (anger, contempt, disgust, fear, happy, sad and surprised) and resulted in 89.9% mean class recognition accuracy.