Abstract
We consider the face recognition task where facial images of the same identity (person) is expected to be closer
in the representation space, while different identities be
far apart. Several recent studies encourage the intra-class
compactness by developing loss functions that penalize the
variance of representations of the same identity. In this paper, we propose the ‘exclusive regularization’ that focuses
on the other aspect of discriminability – the inter-class separability, which is neglected in many recent approaches.
The proposed method, named RegularFace, explicitly distances identities by penalizing the angle between an identity and its nearest neighbor, resulting in discriminative face
representations. Our method has intuitive geometric interpretation and presents unique benefits that are absent in
previous works. Quantitative comparisons against prior
methods on several open benchmarks demonstrate the superiority of our method. In addition, our method is easy to
implement and requires only a few lines of python code on
modern deep learning frameworks.