Isometric deformation modeling using singular value decomposition for 3D expression-invariant face recognition

Currently, the recognition of faces under varying expressions is one of the main challenges in the face recognition community. In this paper a method is presented dealing with those expression variations by using an isometric deformation model. The method is built upon the geodesic distance matrix as a representation of the 3D face. We will show that the set of largest singular values is an excellent expression-invariant shape descriptor. Face comparison is performed by comparison of their shape descriptors using the mean normalized Manhattan distance as dissimilarity measure. The presented method is validated on a subset of 900 faces of the BU-3DFE face database resulting in an equal error rate of 13.37% for the verification scenario. This result is comparable with the equal error rates of other 3D expression-invariant face recognition methods using an isometric deformation model on the same database.