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Facial expression recognition has many potential applications which has attracted the attention of researchers in the last decade. Feature extraction is one important step in expression analysis which contributes toward fast and accurate expression recognition. This paper represents an approach of combining the shape and appearance features to form a hybrid feature vector. We have extracted Pyramid...
Facial expressions are the most informative activities on the human face. Facial movements can be analyzed in order to recognize facial expressions. These movements are extracted using the facial geometry or texture. The paper addresses the facial expression recognition based on Local Binary Patterns (LBP) extracted from the texture information. The LBP operator and its extensions are applied to different...
The Local Binary Patterns (LBP) feature extraction method is a theoretically and computationally simple and efficient methodology for texture analysis. The LBP operator is used in many applications such as facial expression recognition and face recognition. The original LBP is based on hard thresholding the neighborhood of each pixel, which makes texture representation sensitive to noise. In addition,...
A novel approach to facial expression recognition (FER) based on the combination of non-negative matrix factorization (NMF) and support vector machine (SVM) was proposed. One key step in FER is to extract expression features from the original face images. NMF is an effective approach to extract expression features because NMF decomposition makes the reconstruction of expression images in a non-subtractive...
A novel approach to facial expression recognition based on the combination of local binary pattern (LBP) and Adaboost is proposed. Firstly, facial expression images are processed with LBP operator, which can eliminate the effect of environment lighting in a certain extent and has the powerful capability of texture feature description. And then facial expression features are presented with LBP histograms...
During the driving, the good emotion can benefit the vehicle safety. The good emotion will result in the certain facial expressions and vice versa. The facial expression is used as a useful cue to perform the surveillance of driverpsilas status. Considering the characteristics of driving safety, the facial expressions of anger, happiness, sadness and fear are investigated. The main contribution of...
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