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  • Küçük Resim
    Öğe
    Feature Selection for Multi-SVM Classifiers in Facial Expression Classification
    (Ieee, 2008) Gunes, Turan; Polat, Ediz
    Facial expressions are non-verbal signs that play an important role between interpersonal communications and also they are the most effective way to describe human emotions. The correct and fast extraction/recognition of the facial expressions is an ongoing research area for computer vision. In this study, the effects of feature selection on the classification of seven different facial expressions (anger, disgust, fear, joy, neutral, sadness, and surprise) are analyzed. To this end, the features for each expression were extracted using Gabor filters from the facial images and selected the best ones using two different feature categories. In the first category, the features that they do exist in one class but do not exist in all other classes have been selected. In the second category, the features that they represent the one class have been selected. These selected features were employed for expression classification using Support Vector Machines (SVMs). Comparative results are given in terms of types for selecting features, feature selection algorithms and the approaches used for the multi-classification. The best results were obtained using RFE algorithm with first type of feature selection and using one-vs-rest approach for training. It was also demonstrated that the feature selection has a positive effect to increase the classification performance comparing when the results were observed with no feature selection.
  • Küçük Resim
    Öğe
    Feature selection in facial expression analysis and its effect on multi-SVM classifiers
    (Gazi Univ, Fac Engineering Architecture, 2009) Gunes, Turan; Polat, Ediz
    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.