  Currently available face datasets mainly consist of a large number of
high-quality and a small number of low-quality samples. As a result, a Face
Recognition (FR) network fails to learn the distribution of low-quality samples
since they are less frequent during training (underrepresented). Moreover,
current state-of-the-art FR training paradigms are based on the
sample-to-center comparison (i.e., Softmax-based classifier), which results in
a lack of uniformity between train and test metrics. This work integrates a
quality-aware learning process at the sample level into the classification
training paradigm (QAFace). In this regard, Softmax centers are adaptively
guided to pay more attention to low-quality samples by using a quality-aware
function. Accordingly, QAFace adds a quality-based adjustment to the updating
procedure of the Softmax-based classifier to improve the performance on the
underrepresented low-quality samples. Our method adaptively finds and assigns
more attention to the recognizable low-quality samples in the training
datasets. In addition, QAFace ignores the unrecognizable low-quality samples
using the feature magnitude as a proxy for quality. As a result, QAFace
prevents class centers from getting distracted from the optimal direction. The
proposed method is superior to the state-of-the-art algorithms in extensive
experimental results on the CFP-FP, LFW, CPLFW, CALFW, AgeDB, IJB-B, and IJB-C
datasets.
