  Deep Convolutional Neural Networks (DCNNs) and their variants have been
widely used in large scale face recognition(FR) recently. Existing methods have
achieved good performance on many FR benchmarks. However, most of them suffer
from two major problems. First, these methods converge quite slowly since they
optimize the loss functions in a high-dimensional and sparse Gaussian Sphere.
Second, the high dimensionality of features, despite the powerful descriptive
ability, brings difficulty to the optimization, which may lead to a sub-optimal
local optimum. To address these problems, we propose a simple yet efficient
training mechanism called MultiFace, where we approximate the original
high-dimensional features by the ensemble of low-dimensional features. The
proposed mechanism is also generic and can be easily applied to many advanced
FR models. Moreover, it brings the benefits of good interpretability to FR
models via the clustering effect. In detail, the ensemble of these
low-dimensional features can capture complementary yet discriminative
information, which can increase the intra-class compactness and inter-class
separability. Experimental results show that the proposed mechanism can
accelerate 2-3 times with the softmax loss and 1.2-1.5 times with Arcface or
Cosface, while achieving state-of-the-art performances in several benchmark
datasets. Especially, the significant improvements on large-scale
datasets(e.g., IJB and MageFace) demonstrate the flexibility of our new
training mechanism.
