  "Frontalization" is the process of synthesizing frontal facing views of faces
appearing in single unconstrained photos. Recent reports have suggested that
this process may substantially boost the performance of face recognition
systems. This, by transforming the challenging problem of recognizing faces
viewed from unconstrained viewpoints to the easier problem of recognizing faces
in constrained, forward facing poses. Previous frontalization methods did this
by attempting to approximate 3D facial shapes for each query image. We observe
that 3D face shape estimation from unconstrained photos may be a harder problem
than frontalization and can potentially introduce facial misalignments.
Instead, we explore the simpler approach of using a single, unmodified, 3D
surface as an approximation to the shape of all input faces. We show that this
leads to a straightforward, efficient and easy to implement method for
frontalization. More importantly, it produces aesthetic new frontal views and
is surprisingly effective when used for face recognition and gender estimation.
