Face recognition using optimal linear components of range images

This paper investigates the use of range images of faces for recognizing people. 3D scans of faces lead to range images that are linearly projected to low-dimensional subspaces for use in a classifier, say a nearest neighbor classifier or a support vector machine, to label people. Learning of subspaces is performed using an optimal component analysis, i.e. a stochastic optimization algorithm (on a Grassmann manifold) to find a subspace that maximizes classifier performance on the training image set. Results are presented for face recognition using FSU face database, and are compared with standard component analysis such as PCA and ICA. This provides an efficient tool for analyzing certain aspects of facial shapes while avoiding a difficult task of geometric surface modeling.