  Deep convolutional neural networks have achieved remarkable success in face
recognition (FR), partly due to the abundant data availability. However, the
current training benchmarks exhibit an imbalanced quality distribution; most
images are of high quality. This poses issues for generalization on hard
samples since they are underrepresented during training. In this work, we
employ the multi-model boosting technique to deal with this issue. Inspired by
the well-known AdaBoost, we propose a sample-level weighting approach to
incorporate the importance of different samples into the FR loss. Individual
models of the proposed framework are experts at distinct levels of sample
hardness. Therefore, the combination of models leads to a robust feature
extractor without losing the discriminability on the easy samples. Also, for
incorporating the sample hardness into the training criterion, we analytically
show the effect of sample mining on the important aspects of current angular
margin loss functions, i.e., margin and scale. The proposed method shows
superior performance in comparison with the state-of-the-art algorithms in
extensive experiments on the CFP-FP, LFW, CPLFW, CALFW, AgeDB, TinyFace, IJB-B,
and IJB-C evaluation datasets.
