3D Face Recognition
Abstract
In this paper, we present a new 3D face recognition approach. Full automa-
tion is provided through the use of advanced multi-stage alignment algo-
rithms, resilience to facial expressions by employing a deformable model
framework, and invariance to 3D capture devices through suitable prepro-
cessing steps. In addition, scalability in both time and space is achieved by
converting 3D facial scans into compact wavelet metadata. We present results
on the largest known, and now publicly-available, Face Recognition Grand
Challenge 3D facial database consisting of several thousand scans. To the
best of our knowledge, our approach has achieved the highest accuracy on
this dataset.
tion is provided through the use of advanced multi-stage alignment algo-
rithms, resilience to facial expressions by employing a deformable model
framework, and invariance to 3D capture devices through suitable prepro-
cessing steps. In addition, scalability in both time and space is achieved by
converting 3D facial scans into compact wavelet metadata. We present results
on the largest known, and now publicly-available, Face Recognition Grand
Challenge 3D facial database consisting of several thousand scans. To the
best of our knowledge, our approach has achieved the highest accuracy on
this dataset.
