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A general FER follows five stages which includes the task of image capturing, the creation of pre-processing techniques, effective feature extraction, recognition and post processing. The usefulness of such a structure depends to a large degree on the exact mechanism of abstraction and classification of features. Even whilst using the best classification model, insufficient extraction of the function will degrade the efficiency. For a reliable FER system, developing a suitable characteristic descriptor is indeed vital.
Techniques for feature extraction process may be generally grouped into two types: handmade features and (Corneanu C. A.et.al,2016) learned features. The handmade features are well before-designed to capture specific facial expressions while the learned features are coded utilizing convolution neural networks (CNN). The CNN based methods (Burkert P.et.al,2015, Mollahosseini A.et.al,2016,Barsoum E.et.al,2016) jointly learn to classify the facial expression through the correct attributes and weights. Handmade features proposed in the existing method broadly comes under appearance based features and geometric features. The geometric features (Pantic M. and Patras I., 2006 & Sebe N et.al.,2007) encode the face image with the help of geometric properties like deformation, contour, and various other geometric properties. Zhang Z. et al. (Zhang Z. et al.1998) represented face image by 34 facial points and utilized them as a landmark points. Further, these landmark points are used to extract geometric features. Valstar M.F.et al.(Valstar M. F. et.al.,2005) proposed to track the facial points and detect the AUs (Action Units) in the face image. The facial expressions can be recognized based on the detected AUs in the image. The geometric features fail to identify the minute characteristics such as ridges and skin texture changes and are dependent on reliable and accurate feature detection and tracking. In addition, pre processing techniques are required to localize various facial components before the extraction of facial features.