  Face recognition has witnessed significant progresses due to the advances of
deep convolutional neural networks (CNNs), the central challenge of which, is
feature discrimination. To address it, one group tries to exploit mining-based
strategies (\textit{e.g.}, hard example mining and focal loss) to focus on the
informative examples. The other group devotes to designing margin-based loss
functions (\textit{e.g.}, angular, additive and additive angular margins) to
increase the feature margin from the perspective of ground truth class. Both of
them have been well-verified to learn discriminative features. However, they
suffer from either the ambiguity of hard examples or the lack of discriminative
power of other classes. In this paper, we design a novel loss function, namely
support vector guided softmax loss (SV-Softmax), which adaptively emphasizes
the mis-classified points (support vectors) to guide the discriminative
features learning. So the developed SV-Softmax loss is able to eliminate the
ambiguity of hard examples as well as absorb the discriminative power of other
classes, and thus results in more discrimiantive features. To the best of our
knowledge, this is the first attempt to inherit the advantages of mining-based
and margin-based losses into one framework. Experimental results on several
benchmarks have demonstrated the effectiveness of our approach over
state-of-the-arts.
