  Face recognition achieves exceptional success thanks to the emergence of deep
learning. However, many contemporary face recognition models still perform
relatively poor in processing profile faces compared to frontal faces. A key
reason is that the number of frontal and profile training faces are highly
imbalanced - there are extensively more frontal training samples compared to
profile ones. In addition, it is intrinsically hard to learn a deep
representation that is geometrically invariant to large pose variations. In
this study, we hypothesize that there is an inherent mapping between frontal
and profile faces, and consequently, their discrepancy in the deep
representation space can be bridged by an equivariant mapping. To exploit this
mapping, we formulate a novel Deep Residual EquivAriant Mapping (DREAM) block,
which is capable of adaptively adding residuals to the input deep
representation to transform a profile face representation to a canonical pose
that simplifies recognition. The DREAM block consistently enhances the
performance of profile face recognition for many strong deep networks,
including ResNet models, without deliberately augmenting training data of
profile faces. The block is easy to use, light-weight, and can be implemented
with a negligible computational overhead.
