  In this paper, we study the problem of training large-scale face
identification model with imbalanced training data. This problem naturally
exists in many real scenarios including large-scale celebrity recognition,
movie actor annotation, etc. Our solution contains two components. First, we
build a face feature extraction model, and improve its performance, especially
for the persons with very limited training samples, by introducing a
regularizer to the cross entropy loss for the multi-nomial logistic regression
(MLR) learning. This regularizer encourages the directions of the face features
from the same class to be close to the direction of their corresponding
classification weight vector in the logistic regression. Second, we build a
multi-class classifier using MLR on top of the learned face feature extraction
model. Since the standard MLR has poor generalization capability for the
one-shot classes even if these classes have been oversampled, we propose a
novel supervision signal called underrepresented-classes promotion loss, which
aligns the norms of the weight vectors of the one-shot classes (a.k.a.
underrepresented-classes) to those of the normal classes. In addition to the
original cross entropy loss, this new loss term effectively promotes the
underrepresented classes in the learned model and leads to a remarkable
improvement in face recognition performance.
  We test our solution on the MS-Celeb-1M low-shot learning benchmark task. Our
solution recognizes 94.89% of the test images at the precision of 99\% for the
one-shot classes. To the best of our knowledge, this is the best performance
among all the published methods using this benchmark task with the same setup,
including all the participants in the recent MS-Celeb-1M challenge at ICCV
2017.
