Automated human face recognition is a computer vision problem of considerable practical significance. Existing two dimensional (2D) face recognition techniques perform poorly for faces with uncontrolled poses, lighting and facial expressions. Face recognition technology based on three dimensional (3D) facial models is now emerging. Geometric facial models can be easily corrected for pose variations. They are illumination invariant, and provide structural information about the facial surface. Algorithms for 3D face recognition exist, however the area is far from being a matured technology. In this dissertation we address a number of open questions in the area of 3D human face recognition. Firstly, we make available to qualified researchers in the field, at no cost, a large Texas 3D Face Recognition Database, which was acquired as a part of this research work. This database contains 1149 2D and 3D images of 118 subjects. We also provide 25 manually located facial fiducial points on each face in this database. Our next contribution is the development of a completely automatic novel 3D face recognition algorithm, which employs discriminatory anthropometric distances between carefully selected local facial features. This algorithm neither uses general purpose pattern recognition approaches, nor does it directly extend 2D face recognition techniques to the 3D domain. Instead, it is based on an understanding of the structurally diverse characteristics of human faces, which we isolate from the scientific discipline of facial anthropometry. We demonstrate the effectiveness and superior performance of the proposed algorithm, relative to existing benchmark 3D face recognition algorithms. A related contribution is the development of highly accurate and reliable 2D+3D algorithms for automatically detecting 10 anthropometric facial fiducial points. While developing these algorithms, we identify unique structural/textural properties associated with the facial fiducial points. Furthermore, unlike previous algorithms for detecting facial fiducial points, we systematically evaluate our algorithms against manually located facial fiducial points on a large database of images. Our third contribution is the development of an effective algorithm for computing the structural dissimilarity of 3D facial surfaces, which uses a recently developed image similarity index called the complex-wavelet structural similarity index. This algorithm is unique in that unlike existing approaches, it does not require that the facial surfaces be finely registered before they are compared. Furthermore, it is nearly an order of magnitude more accurate than existing facial surface matching based approaches. Finally, we propose a simple method to combine the two new 3D face recognition algorithms that we developed, resulting in a 3D face recognition algorithm that is competitive with the existing state-of-the-art algorithms. / text

- 1 - Abstract The intent of this thesis is to evaluate the differences in facial morphology of children between 3 and 15 years of age based on 3D facial models and cross-sectional data. Due to improper use of cross-sectional data for studying growth, the next part of the thesis is focused on the comparison of cross-sectional and longitudinal approaches in research. The longitudinal observation of facial developmental changes can be considered as actual growth. The cross-sectional database contains 839 3D facial models (397 boys, 442 girls). Three previously published longitudinal databases were used for comparison. Their age intervals were as follows: 3 to 6 years (12 boys, 14 girls), 6 to 12 years (15 boys, 18 girls), 12 to 15 years (23 boys, 22 girls). Geometric morphometric methods were used to analyse facial models (Coherent Point Drift - Dense Correspondance Analysis, Per Vertex T-Test and Principal Component Analysis). The results were visualized using superimposition colour maps, shell distance significance maps and their interlacing. When annual consecutive age intervals were used for cross-sectional data, we could not observe the fluency of differences in facial morphology between age categories, which we can observe during actual growth. When wider age intervals were used for cross-sectional...

- 1 - Abstract The intent of this thesis is to evaluate the differences in facial morphology of children between 3 and 15 years of age based on 3D facial models and cross-sectional data. Due to improper use of cross-sectional data for studying growth, the next part of the thesis is focused on the comparison of cross-sectional and longitudinal approaches in research. The longitudinal observation of facial developmental changes can be considered as actual growth. The cross-sectional database contains 839 3D facial models (397 boys, 442 girls). Three previously published longitudinal databases were used for comparison. Their age intervals were as follows: 3 to 6 years (12 boys, 14 girls), 6 to 12 years (15 boys, 18 girls), 12 to 15 years (23 boys, 22 girls). Geometric morphometric methods were used to analyse facial models (Coherent Point Drift - Dense Correspondance Analysis, Per Vertex T-Test and Principal Component Analysis). The results were visualized using superimposition colour maps, shell distance significance maps and their interlacing. When annual consecutive age intervals were used for cross-sectional data, we could not observe the fluency of differences in facial morphology between age categories, which we can observe during actual growth. When wider age intervals were used for cross-sectional...