  With the development of deep learning, Deep Metric Learning (DML) has
achieved great improvements in face recognition. Specifically, the widely used
softmax loss in the training process often bring large intra-class variations,
and feature normalization is only exploited in the testing process to compute
the pair similarities. To bridge the gap, we impose the intra-class cosine
similarity between the features and weight vectors in softmax loss larger than
a margin in the training step, and extend it from four aspects. First, we
explore the effect of a hard sample mining strategy. To alleviate the human
labor of adjusting the margin hyper-parameter, a self-adaptive margin updating
strategy is proposed. Then, a normalized version is given to take full
advantage of the cosine similarity constraint. Furthermore, we enhance the
former constraint to force the intra-class cosine similarity larger than the
mean inter-class cosine similarity with a margin in the exponential feature
projection space. Extensive experiments on Labeled Face in the Wild (LFW),
Youtube Faces (YTF) and IARPA Janus Benchmark A (IJB-A) datasets demonstrate
that the proposed methods outperform the mainstream DML methods and approach
the state-of-the-art performance.
