This thesis introduces an efficient and robust approach for 3D reconstruction of complete head models with consistent parameterization and personalized shapes from several possible inputs. The system input consists of Cyberware laser-scanned data where we perform scanning task as well as publically available face data where (i) facial expression may or may not exist and (ii) only partial information of head may exist, for instance only front face part without back part of the head. Our method starts with a surface reconstruction approach to either transfer point clouds to a mesh structure or to fill missing points on a triangular mesh. Then, it is followed by a registration process which unifies the representation of all meshes. Afterward, a photo-cloning method is used to extract an adequate set of features in a semi-automatic way on snapshots taken from front and left views of provided range data. We modify Radial Basis Functions (RBFs) deformation so that it would be based on not only distance, but also regional information. Using feature point sets and modified RBFs deformation, a generic mesh can be manipulated in a way that closed eyes and mouth movements like separating upper lip and lower lip can be properly handled. In other word, such mesh modification method makes construction of various facial expressions possible. Moreover, new functions are added where a generic model can be manipulated based on feature point sets to consequently recover missing parts such as ears, back of the head and neck in the input face. After feature-based deformation using modified radial basis functions, a fine mesh modification method based on model points follows to extract the fine details from the available range data. Then, some post refinement procedures employing RBFs deformation and averaging neighboring points are carried out to make the surface of reconstructed 3D head smoother and uniform. Due to existence of flaws and defects on the mesh surface such as flipped triangles, self-intersections or degenerate faces, an automatic repairing approach is leveraged to clean up the entire surface of the mesh. The experiments which are performed on various models show that our method is robust and efficient in terms of accurate full head reconstruction from input data and execution time, respectively. In our method, it is also aimed to use minimum user interaction as much as possible.

This thesis introduces an efficient and robust approach for 3D reconstruction of complete head models with consistent parameterization and personalized shapes from several possible inputs. The system input consists of Cyberware laser-scanned data where we perform scanning task as well as publically available face data where (i) facial expression may or may not exist and (ii) only partial information of head may exist, for instance only front face part without back part of the head. Our method starts with a surface reconstruction approach to either transfer point clouds to a mesh structure or to fill missing points on a triangular mesh. Then, it is followed by a registration process which unifies the representation of all meshes. Afterward, a photo-cloning method is used to extract an adequate set of features in a semi-automatic way on snapshots taken from front and left views of provided range data. We modify Radial Basis Functions (RBFs) deformation so that it would be based on not only distance, but also regional information. Using feature point sets and modified RBFs deformation, a generic mesh can be manipulated in a way that closed eyes and mouth movements like separating upper lip and lower lip can be properly handled. In other word, such mesh modification method makes construction of various facial expressions possible. Moreover, new functions are added where a generic model can be manipulated based on feature point sets to consequently recover missing parts such as ears, back of the head and neck in the input face. After feature-based deformation using modified radial basis functions, a fine mesh modification method based on model points follows to extract the fine details from the available range data. Then, some post refinement procedures employing RBFs deformation and averaging neighboring points are carried out to make the surface of reconstructed 3D head smoother and uniform. Due to existence of flaws and defects on the mesh surface such as flipped triangles, self-intersections or degenerate faces, an automatic repairing approach is leveraged to clean up the entire surface of the mesh. The experiments which are performed on various models show that our method is robust and efficient in terms of accurate full head reconstruction from input data and execution time, respectively. In our method, it is also aimed to use minimum user interaction as much as possible.