Before revision: 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).
After revision: 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 ] ] geometric structure when training embedding models for one-shot learning (e. g., for face verification).