  In this work, we present an unconstrained face verification algorithm and
evaluate it on the recently released IJB-A dataset that aims to push the
boundaries of face verification methods. The proposed algorithm couples a deep
CNN-based approach with a low-dimensional discriminative embedding learnt using
triplet similarity constraints in a large margin fashion. Aside from yielding
performance improvement, this embedding provides significant advantages in
terms of memory and post-processing operations like hashing and visualization.
Experiments on the IJB-A dataset show that the proposed algorithm outperforms
state of the art methods in verification and identification metrics, while
requiring less training time.
