  Face recognition has witnessed significant progress due to the advances of
deep convolutional neural networks (CNNs), the central task of which is how to
improve the feature discrimination. To this end, several margin-based
(\textit{e.g.}, angular, additive and additive angular margins) softmax loss
functions have been proposed to increase the feature margin between different
classes. However, despite great achievements have been made, they mainly suffer
from three issues: 1) Obviously, they ignore the importance of informative
features mining for discriminative learning; 2) They encourage the feature
margin only from the ground truth class, without realizing the discriminability
from other non-ground truth classes; 3) The feature margin between different
classes is set to be same and fixed, which may not adapt the situations very
well. To cope with these issues, this paper develops a novel loss function,
which adaptively emphasizes the mis-classified feature vectors to guide the
discriminative feature learning. Thus we can address all the above issues and
achieve more discriminative face features. To the best of our knowledge, this
is the first attempt to inherit the advantages of feature margin and feature
mining into a unified loss function. Experimental results on several benchmarks
have demonstrated the effectiveness of our method over state-of-the-art
alternatives.

Computer Vision and Pattern Recognition