Gunes, TuranPolat, Ediz2020-06-252020-06-252009Güneş, T., Polat, E. (2009). Yüz ifade analizinde öznitelik seçimi ve çoklu SVM sınıflandırıcılarına etkisi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 24(1), 7 - 14.1300-18841304-4915https://hdl.handle.net/20.500.12587/4502Facial expressions are non-verbal signs that play important role to provide complete meaning in human communication. While humans can easily comprehend the facial expressions, it is not valid for the computers, thus the researchers are still working on developing reliable facial expression recognition systems. In this research, the analysis of 7 different human facial expressions (anger, disgust, fear, happiness, neutral, sadness and surprise) is performed from human facial images. For this purpose, the features for every facial expression are extracted using Gabor filters. The feature sets that best represent the facial expressions are obtained using different feature selection algorithms. The effects of selected feature sets on the multi-class Support Vector Machine (SVM) classifiers are investigated and a comparative evaluation for classification results is given for each algorithm. For the multi-class classification, the SVM classifier is used with three different approaches including One-Vs-One, One-Vs-Rest and Multi-class SVM. It is also shown that classification rates are increased when the selected features are used.trinfo:eu-repo/semantics/openAccessFacial expression analysisfeature extractiongabor filterfeature selectionsupport vector machinesclassificationArticle2417142-s2.0-65349149447Q2trdizinikyokturWOS:000273608200002N/A