3D Computational Archaeology and Face Synthesis using Novel Surface Modeling and Registration Techniques

In this thesis we present two applications where 3D surface modeling and registration play a role of paramount importance. The first application is in archaeology, where our proposed method assists the archaeologist in the tedious process of reconstructing ceramic vessels from the excavated fragments (or shards). The method takes advantage of the surface intrinsic features coupled with a library of generic models constructed by the archaeologists to produce a virtual reconstruction of what the original vessel may have looked like. Generic models are generated based on the expert historical knowledge of the period, provenance of the artifact, and location of the digging site. The generic models need not to be identical to the original vessel, but must be within a geometric transformation of it in most of its parts. The methods are suited for ceramic vessels with distinctive color patterns or reliefs (i.e., surface with molding, carving, or stamping). We introduce a novel curvature B-spline fitting method that makes the extraction of robust features possible. The alignment of the shards against the generic model uses a novel set of 3D weighted curve moments and absolute affine invariants derived from them. The transformation is computed from corresponding parabolic contours on the shard and the generic model. When the shards have no color or relief on them, they are virtually mended to abutting shards using intrinsic differential anchor points computed on the shards break curves and a set of absolute invariants. For axially symmetric vessels, a global constraint induced by the surface of revolution is applied to guarantee global mending consistency. In our experiment involving around 300 shards, more than 85% of them are successfully mended. The mended shards consist of over 90% surface areas of the ceramic vessels.;The second application is in 3D human face synthesis and recognition, where our proposed method assists the national security agencies in constructing a 3D face database to be used for spotting missing personnel or identify possible suspects when close up images of that person are flagged up. A gender/race generic 3D face model is morphed into a personalized unique 3D face of a given person from using a limited number of 2D images of the given person taken from different viewpoints. The 2D images need not to be of the same resolution, or taken under similar brightness or same place, or even same time period. The method applies also to drawing sketches of the person in question. The reconstructed 3D personalized model is an accurate rendition of the actual human face, and the reconstruction errors are smaller than a 3D scanner resolution that is used to scan the person's face. Our method renders a difficult 2D face recognition problem into a much simpler 3D surface registration (alignment) problem. Given a set of images of a test person, a personalized 3D face model is constructed and "best" aligned with one of the 3D faces stored in the database. When the minimum alignment errors are below an accepted threshold (lower than the scanner resolution), the identity of the test person is matched with the one in the database. Experimental results are shown in controlled environment with high resolution images, under ideal illumination condition, as well as for images taken in uncontrolled environment with low resolution, arbitrary illumination. In both conditions, the recognition rate is over 95%. It is also proved that better face recognition rate is achieved by using a gender/race based generic 3D face model which allows for accurate pose estimation as well as the face identification in 3D.