  Despite significant progress made over the past twenty five years,
unconstrained face verification remains a challenging problem. This paper
proposes an approach that couples a deep CNN-based approach with a
low-dimensional discriminative embedding learned using triplet probability
constraints to solve the unconstrained face verification problem. Aside from
yielding performance improvements, this embedding provides significant
advantages in terms of memory and for post-processing operations like subject
specific clustering. Experiments on the challenging IJB-A dataset show that the
proposed algorithm performs comparably or better than the state of the art
methods in verification and identification metrics, while requiring much less
training data and training time. The superior performance of the proposed
method on the CFP dataset shows that the representation learned by our deep CNN
is robust to extreme pose variation. Furthermore, we demonstrate the robustness
of the deep features to challenges including age, pose, blur and clutter by
performing simple clustering experiments on both IJB-A and LFW datasets.
