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Learning from Millions of 3D Scans for Large-scale 3D Face Recognition
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.Comment: 11 page
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