Abstract
Deep networks trained on millions of facial images are
believed to be closely approaching human-level performance in face recognition. However, open world face
recognition still remains a challenge. Although, 3D face
recognition has an inherent edge over its 2D counterpart,
it has not benefited from the recent developments in deep
learning due to the unavailability of large training as well
as large test datasets. Recognition accuracies have already
saturated on existing 3D face datasets due to their small
gallery sizes. Unlike 2D photographs, 3D facial scans
cannot be sourced from the web causing a bottleneck in
the development of deep 3D face recognition networks and
datasets. In this backdrop, we propose a method for generating a large corpus of labeled 3D face identities and their
multiple instances for training and a protocol for merging
the most challenging existing 3D datasets for testing. We
also propose the first deep CNN model designed specifically
for 3D face recognition and trained on 3.1 Million 3D facial scans of 100K identities. Our test dataset comprises
1,853 identities with a single 3D scan in the gallery and another 31K scans as probes, which is several orders of magnitude larger than existing ones. Without fine tuning on this
dataset, our network already outperforms state of the art
face recognition by over 10%. We fine tune our network on
the gallery set to perform end-to-end large scale 3D face
recognition which further improves accuracy. Finally, we
show the efficacy of our method for the open world face
recognition problem