Extraction and selection of muscle based features for facial expression recognition
Benli, Kristin Surpuhi
Eskil, Mustafa Taner
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In this study we propose a new set of muscle activity based features for facial expression recognition. We extract muscular activities by observing the displacements of facial feature points in an expression video. The facial feature points are initialized on muscular regions of influence in the first frame of the video. These points are tracked through optical flow in sequential frames. Displacements of feature points on the image plane are used to estimate the 3D orientation of a head model and relative displacements of its vertices. We model the human skin as a linear system of equations. The estimated deformation of the wireframe model produces an over-determined system of equations that can be solved under the constraint of the facial anatomy to obtain muscle activation levels. We apply sequential forward feature selection to choose the most descriptive set of muscles for recognition of basic facial expressions.
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Benli, Kristin Surpuhi; Eskil, Mustafa Taner (IEEE, 2014-04-23)In this study we propose a set of anatomy based features for facial expression recognition. The muscle forces that constitute an expression are solved by tracking carefully selected facial feature points. These points are ...
Eskil, Mustafa Taner; Benli, Kristin Surpuhi (Academic Press INC Elsevier Science, 2014-02)In this study, we propose a novel approach to facial expression recognition that capitalizes on the anatomical structure of the human face. We model human face with a high-polygon wireframe model that embeds all major ...
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