LBP and SIFT based Facial Expression Recognition


SUMER O., Güneş E. O.

7th International Conference on Machine Vision (ICMV), Milan, İtalya, 19 - 21 Kasım 2014, cilt.9445 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 9445
  • Doi Numarası: 10.1117/12.2181505
  • Basıldığı Şehir: Milan
  • Basıldığı Ülke: İtalya
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

This study compares the performance of local binary patterns (LBP) and scale invariant feature transform (SIFT) with support vector machines (SVM) in automatic classification of discrete facial expressions. Facial expression recognition is a multiclass classification problem and seven classes; happiness, anger, sadness, disgust, surprise, fear and comtempt are classified. Using SIFT feature vectors and linear SVM, 93.1% mean accuracy is acquired on CK+ database. On the other hand, the performance of LBP-based classifier with linear SVM is reported on SFEW using strictly person independent (SPI) protocol. Seven-class mean accuracy on SFEW is 59.76%. Experiments on both databases showed that LBP features can be used in a fairly descriptive way if a good localization of facial points and partitioning strategy are followed.