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, TuftsFace, 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 .
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. We analyze the performance of our methods on nine different (CK+, PubFig, Tufts, VggFace2, LFW, YTF, MegaFace, IJB-B, IJB-C). Our results consistently show that the additional structure encoded about each face's emotions results in an embedding model that gives higher verification accuracy than training with just ArcFace or Triplet Loss alone. Moreover, the method is significantly more accurate than a simple multi-task learning approach . 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 .
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For the task of face verification, we
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labels (e.g., facial expression) to impose
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than training with just ArcFace or Triplet Loss alone. Moreover, the method is significantly more accurate than a simple multi-task learning approach
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