  We show how a simple convolutional neural network (CNN) can be trained to
accurately and robustly regress 6 degrees of freedom (6DoF) 3D head pose,
directly from image intensities. We further explain how this FacePoseNet (FPN)
can be used to align faces in 2D and 3D as an alternative to explicit facial
landmark detection for these tasks. We claim that in many cases the standard
means of measuring landmark detector accuracy can be misleading when comparing
different face alignments. Instead, we compare our FPN with existing methods by
evaluating how they affect face recognition accuracy on the IJB-A and IJB-B
benchmarks: using the same recognition pipeline, but varying the face alignment
method. Our results show that (a) better landmark detection accuracy measured
on the 300W benchmark does not necessarily imply better face recognition
accuracy. (b) Our FPN provides superior 2D and 3D face alignment on both
benchmarks. Finally, (c), FPN aligns faces at a small fraction of the
computational cost of comparably accurate landmark detectors. For many
purposes, FPN is thus a far faster and far more accurate face alignment method
than using facial landmark detectors.
