Flexible and Robust 3D Face Recognition

Face recognition is one of the least intrusive modalities in biometrics. Our research shows that using 3D face data for recognition provides a promising route to improved performance. In this dissertation, we introduce a new system for 3D face recognition that addresses many of the current challenges preventing this technology from becoming viable. The first challenge is to determine the feasibility of combining data from multiple sensors. Next, we create a technique named Region Ensemble for FacE Recognition (REFER) that is capable of matching 3D face images in the presence of expressions and occlusion. Accurate feature detection and pose recognition is accomplished through a novel technique that we have created, named Rotated Profile Signatures (RPS). Scalability issues are mitigated by combining a feature-based indexing technique with desktop grid processing to greatly reduce the amount of time required for recognition experiments. Finally, we investigate the potential benefits of using a multi-instance enrollment approach that can be used to further increase the performance of our system. These solutions result in a final system that is capable of deployment in a variety of realistic biometric scenarios.