  In this work, we investigate several methods and strategies to learn deep
embeddings for face recognition, using joint sample- and set-based
optimization. We explain our framework that expands traditional learning with
set-based supervision together with the strategies used to maintain set
characteristics. We, then, briefly review the related set-based loss functions,
and subsequently propose a novel Max-Margin Loss which maximizes maximum
possible inter-class margin with assistance of Support Vector Machines (SVMs).
It implicitly pushes all the samples towards correct side of the margin with a
vector perpendicular to the hyperplane and a strength exponentially growing
towards to negative side of the hyperplane. We show that the introduced loss
outperform the previous sample-based and set-based ones in terms verification
of faces on two commonly used benchmarks.
