Before revision:  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) .
After revision:  We contrast their performances (AUC) on four different face datasets (CK+, PubFig, Tufts, VggFace2, LFW, YTF, MegaFace, IJB-B, IJB-C). Our results consistently show that 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) .