Distinguishing facial expression using the Fisher-Rao metric
The aim in this paper is to explore whether the Fisher-Rao metric can be used to characterise the shape changes due to differences expression. We work with a representation of facial shape based on fields of surface normals. Using the von- Mises Fisher distribution, we compute the elements of the Fisher information matrix, and use this to compute geodesic distance between fields of surface normals. We embed the fields of facial surface normals into a low dimensional pattern space using a number of alternative methods including multidimensional scaling, heat kernel embedding and commute time embedding. We present results on clustering the embedded faces using the BU-3DFEDB database.