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The fourth one includes the information of significant facial components.
Facial components like noses, eyes and hairstyles can be displayed on one side of the screen, and users click on the images to construct a face.
Fig. 8 a Cropping facial components in the CK+ dataset.
They include most facial components like eyes, ears, and mouth.
The usually extracted facial features are either geometric features such as the shapes of the facial components (eyes, mouth, etc).
Facial components are extracted using parametric curves specific to each component as in [72], and facial landmarks are traced on these curves.
The technique of multiple region segmentation is also used when working with facial components.
Local facial components contain more discriminative information and outperform global features for face recognition [15].
The shape of various facial components due to changing emotions can also be extracted.
The facial components were then fused to determine the sparse representation error.
The next step is training these bases into localized facial components by Non-negative matrix factorization.
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