| This dissertation takes a small but important step towards solving the following general problem of current interest: Assuming that each individual in a population can be modeled by a single frontal RGBD face image, is it possible to carry out face recognition in the wild for such a population? Face recognition in the wild means basing the identity decision on a set of images captured by a network of cameras, with each camera viewing the face from an arbitrary viewpoint.;The general problem as stated above is extremely challenging. It, however, throws up subproblems that can be addressed today. The subproblems addressed in this dissertation relate to: (1) Generating for each individual a large set of viewpoint dependent face images from a single RGBD frontal image; (2) Discovering through a comparative evaluation the best algorithm among ISOMAP, LLE, and LPCA to use for clustering the viewpoint-dependent model data so generated on the manifolds on which it resides; (3) Comparing a global approach to classification in which all of the training data resides in the same subspace with hierarchical approaches based on view-partitioned subspaces for representing the training data; and (4) Using a weighted voting algorithm for integrating the evidence collected from multiple images of the same face as recorded from different viewpoints.;For the results shown, the frontal RGBD image of each individual in a collection of 10 people is used to generate 925 viewpoint dependent face images. Although this constitutes a small population, our results nonetheless provide important insights for further extensions of this research. |
