Human face recognition is one of the most important research areas in
biometrics. However, the robust face recognition under a drastic change of the
facial pose, expression, and illumination is a big challenging problem for its
practical application. Such variations make face recognition more difficult. In
this paper, we propose a novel face recognition method, called Attentional
Feature-pair Relation Network (AFRN), which represents the face by the relevant
pairs of local appearance block features with their attention scores. The AFRN
represents the face by all possible pairs of the 9x9 local appearance block
features, the importance of each pair is considered by the attention map that
is obtained from the low-rank bilinear pooling, and each pair is weighted by
its corresponding attention score. To increase the accuracy, we select top-K
pairs of local appearance block features as relevant facial information and
drop the remaining irrelevant. The weighted top-K pairs are propagated to
extract the joint feature-pair relation by using bilinear attention network. In
experiments, we show the effectiveness of the proposed AFRN and achieve the
outstanding performance in the 1:1 face verification and 1:N face
identification tasks compared to existing state-of-the-art methods on the
challenging LFW, YTF, CALFW, CPLFW, CFP, AgeDB, IJB-A, IJB-B, and IJB-C
datasets.

本论文提出了一种名为 AFRN 的人脸识别方法，它是通过考虑注意力分数来表示本地外观块特征的相关配对，以增加准确性并通过使用双线性注意力网络提取联合特征 - 对关系。在实验中，该方法在诸多数据集上的表现均超越了现有的最先进的方法。