  Convolutional neural networks have achieved great improvement on face
recognition in recent years because of its extraordinary ability in learning
discriminative features of people with different identities. To train such a
well-designed deep network, tremendous amounts of data is indispensable. Long
tail distribution specifically refers to the fact that a small number of
generic entities appear frequently while other objects far less existing.
Considering the existence of long tail distribution of the real world data,
large but uniform distributed data are usually hard to retrieve. Empirical
experiences and analysis show that classes with more samples will pose greater
impact on the feature learning process and inversely cripple the whole models
feature extracting ability on tail part data. Contrary to most of the existing
works that alleviate this problem by simply cutting the tailed data for uniform
distributions across the classes, this paper proposes a new loss function
called range loss to effectively utilize the whole long tailed data in training
process. More specifically, range loss is designed to reduce overall
intra-personal variations while enlarging inter-personal differences within one
mini-batch simultaneously when facing even extremely unbalanced data. The
optimization objective of range loss is the $k$ greatest range's harmonic mean
values in one class and the shortest inter-class distance within one batch.
Extensive experiments on two famous and challenging face recognition benchmarks
(Labeled Faces in the Wild (LFW) and YouTube Faces (YTF) not only demonstrate
the effectiveness of the proposed approach in overcoming the long tail effect
but also show the good generalization ability of the proposed approach.
