  To achieve good performance in face recognition, a large scale training
dataset is usually required. A simple yet effective way to improve recognition
performance is to use a dataset as large as possible by combining multiple
datasets in the training. However, it is problematic and troublesome to naively
combine different datasets due to two major issues. First, the same person can
possibly appear in different datasets, leading to an identity overlapping issue
between different datasets. Naively treating the same person as different
classes in different datasets during training will affect back-propagation and
generate non-representative embeddings. On the other hand, manually cleaning
labels may take formidable human efforts, especially when there are millions of
images and thousands of identities. Second, different datasets are collected in
different situations and thus will lead to different domain distributions.
Naively combining datasets will make it difficult to learn domain invariant
embeddings across different datasets. In this paper, we propose DAIL:
Dataset-Aware and Invariant Learning to resolve the above-mentioned issues. To
solve the first issue of identity overlapping, we propose a dataset-aware loss
for multi-dataset training by reducing the penalty when the same person appears
in multiple datasets. This can be readily achieved with a modified softmax loss
with a dataset-aware term. To solve the second issue, domain adaptation with
gradient reversal layers is employed for dataset invariant learning. The
proposed approach not only achieves state-of-the-art results on several
commonly used face recognition validation sets, including LFW, CFP-FP, and
AgeDB-30, but also shows great benefit for practical use.
