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The combination of these two types of features is used for facial expression classification.
In this paper, sparse representation is used for facial expression processing.
For simplicity, the nearest neighbor (1-NN) classifier with the Euclidean metric is used for facial expression classification.
This paper presents a robust and adaptable facial feature extraction system used for facial expression recognition in human-computer interaction (HCI) environments.
The current study evaluates the proposed algorithm using the JAFFE database (Figure 5) which is commonly used for facial expression recognition tasks.
The representative classifiers used for facial expression recognition are neural networks [14], the nearest neighbor (1-NN) [15] or k-nearest neighbor (KNN) classifier [16], and support vector machines (SVM) [17], etc.
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A similar but effective variant of this process is described in [18] where Shan et al. use LBP for facial expression recognition adopting boosted SVM.
21– 23 Autologous fat transfer has been successfully used for facial lipodystrophy and for facial sculpturing in esthetic facial surgery.
The Cohn-Kanade database[28], which is widely used in literature for facial expression analysis, consists of approximately 500 image sequences from posers.
The humanoid robot used in this experiment, WE-4RII, has 59 degrees of freedom (DOFs), 26 of which are specifically used for controlling facial expression (eyebrows: 8; eyelids: 6; eyes: 3; lips: 4; jaw: 1; neck: 4).
Commonly, VS imaging has been used for modeling facial expressions, and associated robust facial recognition techniques have been developed [28 30].
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