  The field of face recognition (FR) has witnessed great progress with the
surge of deep learning. Existing methods mainly focus on extracting
discriminative features, and directly compute the cosine or L2 distance by the
point-to-point way without considering the context information. In this study,
we make a key observation that the local con-text represented by the
similarities between the instance and its inter-class neighbors1plays an
important role forFR. Specifically, we attempt to incorporate the local
in-formation in the feature space into the metric, and pro-pose a unified
framework calledInter-class DiscrepancyAlignment(IDA), with two dedicated
modules, Discrepancy Alignment Operator(IDA-DAO) andSupport Set
Estimation(IDA-SSE). IDA-DAO is used to align the similarity scores considering
the discrepancy between the images and its neighbors, which is defined by
adaptive support sets on the hypersphere. For practical inference, it is
difficult to acquire support set during online inference. IDA-SSE can provide
convincing inter-class neighbors by introducing virtual candidate images
generated with GAN. Further-more, we propose the learnable IDA-SSE, which can
implicitly give estimation without the need of any other images in the
evaluation process. The proposed IDA can be incorporated into existing FR
systems seamlessly and efficiently. Extensive experiments demonstrate that this
frame-work can 1) significantly improve the accuracy, and 2) make the model
robust to the face images of various distributions.Without bells and whistles,
our method achieves state-of-the-art performance on multiple standard FR
benchmarks.
