Low-level fusion and deformation modeling for textured 3D face biometrics

[Truncated abstract] Automatic face recognition has many crucial applications in a range of domains including human-machine interaction and security. The non-intrusive nature of face recognition is a key reason behind its suitability to such a broad range of applications. However, often these applications require high recognition accuracies and robustness which are challenging to achieve due to variations. Variations in pose, facial expression and illumination conditions may obscure the essential visual and/or geometric cues for recognition. The ability of a face recognition system to represent and match faces such that the utilized descriptive cues outweigh the associated variations is a key factor in enhancing its accuracy and robustness. This thesis presents novel algorithms that strive to achieve this goal by combining the descriptive cues in textured 3D facial scans at lower levels (data and feature fusion levels) and/or modeling the facial expression and illumination variations. An approach for combining fields of weak local and global geometrical cues (computed for each scan vertex) at the feature level was devised and used for compact representation and matching of 3D faces. The extracted fields are independent of the coordinate system in which the scan vertices were defined and hence the representation is tolerant to minor pose variations. The combined representation has demonstrated considerably high descriptiveness. In another presented approach, only local regions around key-points on the facial scans were considered. Heuristic measures for the descriptiveness of 3D local regions were defined and used for robust matching. The matching enforces consistent 3D rigid transformations and global structures among matched scans but on the other hand it allows for few mismatches among the local regions. Hence, it avoids the effects of deformed regions on matching. While the above strategies of combining information with and without the avoidance of the