  For the task of face verification, we explore the utility of harnessing
auxiliary facial emotion labels to impose explicit geometric constraints on the
embedding space when training deep embedding models. We introduce several novel
loss functions that, in conjunction with a standard Triplet Loss [43], or
ArcFace loss [10], provide geometric constraints on the embedding space; the
labels for our loss functions can be provided using either manually annotated
or automatically detected auxiliary emotion labels. Our method is implemented
purely in terms of the loss function and does not require any changes to the
neural network backbone of the embedding function.
