  Face recognition has achieved significant progress in deep learning era due
to the ultra-large-scale and welllabeled datasets. However, training on the
outsize datasets is time-consuming and takes up a lot of hardware resource.
Therefore, designing an efficient training approach is indispensable. The heavy
computational and memory costs mainly result from the million-level
dimensionality of thefully connected (FC) layer. To this end, we propose a
novel training approach, termed Faster Face Classification (F2C), to alleviate
time and cost without sacrificing the performance. This method adopts Dynamic
Class Pool (DCP) for storing and updating the identities features dynamically,
which could be regarded as a substitute for the FC layer. DCP is efficiently
time-saving and cost-saving, as its smaller size with the independence from the
whole face identities together. We further validate the proposed F2C method
across several face benchmarks and private datasets, and display comparable
results, meanwhile the speed is faster than state-of-the-art FC-based methods
in terms of recognition accuracy and hardware costs. Moreover, our method is
further improved by a well-designed dual data loader including indentity-based
and instancebased loaders, which makes it more efficient for the updating DCP
parameters.
