Soylemez, Omer FamkErgen, BurhanSoylemez, Nesrin Hark2024-04-242024-04-242017978-1-5090-6494-62165-0608https://hdl.handle.net/11468/2048725th Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2017 -- Antalya, TURKEYIn this study, an SVM-based system is proposed for the classification of facial expressions that are represented in 3D. Distance based features are used as a feature vector, which are determined by the distances between the different key points on the image. Study was conducted on a subset (Happy, sadness, surprise) of Bosphorus 3D Face Database. 9 different fiducial points arc used to calculate a total of 5 distance features. SVM classification was performed with K-fold cross validation thus mean classification performance of different training and test clusters were determined. %85 success rate has achieved as a result of the expression analysis performed on the 3D facial scans.trinfo:eu-repo/semantics/closedAccessFacial Expression RecognitionDistance Based FeaturesSupport Vector MachinesConference ObjectWOS:0004138131004602-s2.0-85026312181N/AN/A