  The quality of face images significantly influences the performance of
underlying face recognition algorithms. Face image quality assessment (FIQA)
estimates the utility of the captured image in achieving reliable and accurate
recognition performance. In this work, we propose a novel learning paradigm
that learns internal network observations during the training process. Based on
that, our proposed CR-FIQA uses this paradigm to estimate the face image
quality of a sample by predicting its relative classifiability. This
classifiability is measured based on the allocation of the training sample
feature representation in angular space with respect to its class center and
the nearest negative class center. We experimentally illustrate the correlation
between the face image quality and the sample relative classifiability. As such
property is only observable for the training dataset, we propose to learn this
property from the training dataset and utilize it to predict the quality
measure on unseen samples. This training is performed simultaneously while
optimizing the class centers by an angular margin penalty-based softmax loss
used for face recognition model training. Through extensive evaluation
experiments on eight benchmarks and four face recognition models, we
demonstrate the superiority of our proposed CR-FIQA over state-of-the-art
(SOTA) FIQA algorithms.
