  We explore the utility of harnessing auxiliary labels (e.g., facial
expression) to impose geometric structure when training embedding models for
one-shot learning (e.g., for face verification). We introduce novel geometric
constraints on the embedding space learned by a deep model using either
manually annotated or automatically detected auxiliary labels. We contrast
their performances (AUC) on four different face datasets(CK+, VGGFace-2, Tufts
Face, and PubFig). Due to the additional structure encoded in the embedding
space, our methods provide a higher verification accuracy (99.7, 86.2, 99.4,
and 79.3% with our proposed TL+PDP+FBV loss, versus 97.5, 72.6, 93.1, and 70.5%
using a standard Triplet Loss on the four datasets, respectively). Our method
is implemented purely in terms of the loss function. It does not require any
changes to the backbone of the embedding functions.
