Human Expression Recognition using Facial Shape Based Fourier Descriptors Fusion
This work addresses the problem of robust dynamic facial expression recognition for applications in social networks and security systems, offering an incremental improvement over existing methods.
This paper proposes a new method for dynamic facial expression recognition by analyzing changes in facial muscles using geometric features for facial regions (mouth, eyes, nose). It employs a fusion of generic and elliptic Fourier shape descriptors to represent emotions, achieving competent recognition accuracy on a well-known facial expression dataset using a multi-class support vector machine.
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.