A-Softmax forces face embeddings onto a hypersphere with a tunable angular margin

Liu et al. modify softmax into a purely angle-based metric by normalizing classifier weights and removing bias, then multiplicatively compressing the target class's angular region by an integer margin m [liu-etal-2017-sphereface] The learned features construct an angular distance metric equivalent to geodesic distance on a hypersphere, a geometry imposed by the normalization rather than discovered in free activations [liu-etal-2017-sphereface] Sweeping m through 1-4 monotonically improves face verification (LFW 97.88% to 99.42%, YTF 93.1% to 95.0%) and open-set MegaFace identification (Rank-1 54.86% to 72.73%) [liu-etal-2017-sphereface] The 64-layer m=4 model trained only on CASIA-WebFace beats Softmax+Center-Loss and rivals models trained on far larger private datasets [liu-etal-2017-sphereface]