This master thesis examines the use of a multi resolution Active Shape Model (ASM) applied on facial features, utilizing the Viola/Jones face detector. The method, initially introduced by Cootes, et al., requires good initial pose parameter values for placing a face model from its local system to the image’s system. This is one of the most critical parts of the process from which the convergence of the method depends on. For this reason, the Viola/Jones detector kicked in, to initially detect the face and subsequently estimate the initial pose parameters for positioning the face model in the search image. The testing of the face detector as well as the quality of the model’s initial position was executed on face images provided by the Milborrow University of Cape Town (MUCT) online database. For building a face model, a set of training images provided by Cootes was used and the search images were chosen randomly from the same training set.
Experiments made initially on some frontal upright images, showed that the face detector succeeded in all images and the placement of the face model was quite accurate in most cases. Subsequently, the quality of the model fit using the multi resolution active shape model approach, showed that the method converged quite well for the inner part of the face but in the outer part, in some cases, was not that precise.

Abstract

This thesis is about creating a number of projective reconstructions of objects, with input data a set of point correspondences measured in a pair of images. Through these points, the Fundamental Matrix is first determined and subsequently, the Essential Matrix through the former. In both cases, 4 different types of reconstructions are created (2 with linear DLT intersections and 2 with non-linear), with the main difference that the reconstructions arising from the Fundamental Matrix lack the internal (calibration) information of the cameras, hence are projectively distorted. In contrast, the reconstruction arising from the Essential Matrix is similar to the real object, since the pair of the projection camera matrices computed includes the interior orientation of the images. To this purpose, algorithms in MatLab have been developed which take as input the coordinates of the corresponding points in two images, calculate first the Fundamental Matrix and subsequently the Essential Matrix from the former. Then, a number of pairs of projective camera matrices are computed, using the Fundamental and Essential Matrices, whereby, in the photogrammetric sense, the projective camera matrices reflect the interior and relative orientations of the cameras. Next, an intersection is programmed using the linear and non linear method of the Direct Linear Triangulation (DLT). In this thesis, the fundamental theoretical background is presented as well as the analysis of the algorithms developed for this purpose. Finally, a presentation, analysis and testing of the application algorithms with simulated and real data is included.