Breaking the 99% barrier: optimisation of three‐dimensional face recognition

Abstract

This study presents optimisations to a three-dimensional (3D) face recognition method the authors published in 2011. The optimisations concern handling and estimation of motion from a single 3D image using the symmetry of the face, fine registration by selection of the maximum score for small variations of the registration parameters and efficient training using automatic outlier removal where only part of the classifier is retrained. The optimisations lead to a staggering performance improvement: the verification rate on Face Recognition Grand Challenge (FRGC) v2 data at false accept rate = 0.1% increases from 94.6 to 99.3% and the identification rate increases from 99 to 99.4%. Both are, to the authors' knowledge, the best scores ever published on the FRGC data. In addition, the registration time was reduced from about 2.5 to 0.2–0.6 s and the number of comparisons has increased from about 11 000 to more than 50 000 per second. For slightly decreased performance, even millions of comparisons can be realised. The fast registration means near real-time recognition with 2–5 images is possible. The optimisations are not specific for this method, but can be beneficial for other 3D face recognition methods as well.