  In the realm of face image quality assesment (FIQA), method based on sample
relative classification have shown impressive performance. However, the quality
scores used as pseudo-labels assigned from images of classes with low
intra-class variance could be unrelated to the actual quality in this method.
To address this issue, we present IG-FIQA, a novel approach to guide FIQA
training, introducing a weight parameter to alleviate the adverse impact of
these classes. This method involves estimating sample intra-class variance at
each iteration during training, ensuring minimal computational overhead and
straightforward implementation. Furthermore, this paper proposes an on-the-fly
data augmentation methodology for improved generalization performance in FIQA.
On various benchmark datasets, our proposed method, IG-FIQA, achieved novel
state-of-the-art (SOTA) performance.
