| Relighting, pose change and recognition of faces from images are intimately connected fundamental problems in the field of Computer Vision and Graphics. These problems are particularly interesting and difficult when examined in presence of constraints like limited number of input images, cast shadows and specularities. Though numerous solutions have been proposed in the past, none effectively addresses these problems when considered in the aforementioned constrained setting. In this dissertation, we present a set of techniques, which accomplish relighting, pose change and recognition of facial images in presence of specularities and shadows, using as few as one input image.;We start by presenting a method for relighting and pose change for facial images using nine or more input images. We accomplish this by representing the Apparent Bidirectional Reflectance Distribution Function (ABRDF) fields of human faces using Tensor Splines. We then present a method for improving the quality of relighted images by enhancing the ABRDFs with face specific subspace representations. Next, we present a novel technique for estimating facial ABRDF fields for the difficult case of single input image. Finally, we focus on the face recognition problem and present a novel face image classification scheme as well as a framework for enhancing face recognition using relighting methods outlined above.;All of the above mentioned techniques are supported by extensive experiments on the Yale A, the CMU PIE, the Extended Yale B and the MERL Dome face image databases. We show that our relighting, pose change and recognition systems outperform various state-of-the-art methods in terms of image quality and recognition rates. |
