We present a 3D face reconstruction system that takes as input either one single view or several different views. Given a facial image, we first classify the facial pose into one of five predefined poses, then detect two anchor points that are then used to detect a set of predefined facial landmarks. Based on these initial steps, for a single view we apply a warping process using a generic 3D face model to build a 3D face. For multiple views, we apply sparse bundle adjustment to reconstruct 3D landmarks which are used to deform the generic 3D face model. Experimental results on the Color FERET and CMU multi-PIE databases confirm our framework is effective in creating realistic 3D face models that can be used in many computer vision applications, such as 3D face recognition at a distance.

While recent years have seen progress in face recognition from 3D images, nonfrontal head pose is still a challenge to existing techniques. We introduce a new system for 3D face recognition that is robust to facial pose variation. Large degrees of facial pose variation may lead to a significant fraction of the features visible in frontal images being occluded. High accuracy automatic feature and pose detection is performed by a new technique called rotated profile signatures (RPS). Experiments are performed on the largest available database of 3D faces acquired under varying pose. This database contains over 7,300 total images of 406 unique subjects gathered at the University of Notre Dame. Experimental results show that the RPS detection algorithm is capable of performing nose detection with greater than 96.5% accuracy across the pose variation represented in the data set used.

In this paper, we introduce a new system for 3D face recognition based on the fusion of results from a committee of regions that have been independently matched. Experimental results demonstrate that using 28 small regions on the face allow for the highest level of 3D face recognition. Score-based fusion is performed on the individual region match scores and experimental results show that the Borda count and consensus voting methods yield higher performance than the standard sum, product, and min fusion rules. In addition, results are reported that demonstrate the robustness of our algorithm by simulating large holes and artifacts in images. To our knowledge, no other work has been published that uses a large number of 3D face regions for high-performance face matching. Rank one recognition rates of 97.2% and verification rates of 93.2% at a 0.1% false accept rate are reported and compared to other methods published on the face recognition grand challenge v2 data set.

Desktop grids have traditionally focused on executing computation intensive workloads. Can they also be used to execute data-intensive workloads? To answer this question, we present a case study of a data intensive biometric application which is infeasible to process on a single machine. We evaluate the capacity of a desktop grid to store and deliver the data need to execute the workload, and compare several general techniques for data deployment. Selecting the most scalable technique, we execute and evaluate five large production workloads on a 350-CPU desktop grid. We observe that this technique is sensitive to many parameters, and propose that an ideal system should be responsible for choosing the proper decomposition of a workload.