  Face recognition (FR) using deep convolutional neural networks (DCNNs) has
seen remarkable success in recent years. One key ingredient of DCNN-based FR is
the appropriate design of a loss function that ensures discrimination between
various identities. The state-of-the-art (SOTA) solutions utilise normalised
Softmax loss with additive and/or multiplicative margins. Despite being
popular, these Softmax+margin based losses are not theoretically motivated and
the effectiveness of a margin is justified only intuitively. In this work, we
utilise an alternative framework that offers a more direct mechanism of
achieving discrimination among the features of various identities. We propose a
novel loss that is equivalent to a triplet loss with proxies and an implicit
mechanism of hard-negative mining. We give theoretical justification that
minimising the proposed loss ensures a minimum separability between all
identities. The proposed loss is simple to implement and does not require heavy
hyper-parameter tuning as in the SOTA solutions. We give empirical evidence
that despite its simplicity, the proposed loss consistently achieves SOTA
performance in various benchmarks for both high-resolution and low-resolution
FR tasks.
