  Dynamic facial expression recognition has many useful applications in social
networks, multimedia content analysis, security systems and others. This
challenging process must be done under recurrent problems of image illumination
and low resolution which changes at partial occlusions. This paper aims to
produce a new facial expression recognition method based on the changes in the
facial muscles. The geometric features are used to specify the facial regions
i.e., mouth, eyes, and nose. The generic Fourier shape descriptor in
conjunction with elliptic Fourier shape descriptor is used as an attribute to
represent different emotions under frequency spectrum features. Afterwards a
multi-class support vector machine is applied for classification of seven human
expression. The statistical analysis showed our approach obtained overall
competent recognition using 5-fold cross validation with high accuracy on
well-known facial expression dataset.
