Feature selection in facial expression analysis and its effect on multi-SVM classifiers
Yükleniyor...
Tarih
2009
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Gazi Univ, Fac Engineering Architecture
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Facial 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.
Açıklama
Anahtar Kelimeler
Facial expression analysis, feature extraction, gabor filter, feature selection, support vector machines, classification
Kaynak
Journal Of The Faculty Of Engineering And Architecture Of Gazi University
WoS Q Değeri
N/A
Scopus Q Değeri
Q2
Cilt
24
Sayı
1
Künye
Güneş, T., Polat, E. (2009). Yüz ifade analizinde öznitelik seçimi ve çoklu SVM sınıflandırıcılarına etkisi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 24(1), 7 - 14.
