Although recent years have seen progress in face recognition from 3D images, non-frontal head pose is still a challenge to existing techniques, so the authors 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. (Publisher abstract provided)