Combining appearance and geometric features for facial expression recognition

This paper introduces a method for facial expression recognition combining appearance and geometric facial features. The proposed framework consistently combines multiple facial representations at both global and local levels. First, covariance descriptors are computed to represent regional features combining various feature information with a low dimensionality. Then geometric features are detected to provide a general facial movement description of the facial expression. These appearance and geometric features are combined to form a vector representation of the facial expression. The proposed method is tested on the CK+ database and shows encouraging performance.