Rotated Profile Signatures for robust 3D feature detection

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.