IG-FIQA: Improving Classifiability-Based Face Image Quality Assessment Through Intra-Class Variance Guidance

This is the authors' abstract. We don't add key points for this paper.

In the realm of face image quality assessment (FIQA), methods 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 such methods. To address this issue, we present intra-class variance guidance for FIQA (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. Across various benchmark datasets, our proposed method, IG-FIQA, achieved notable accuracy improvements compared to conventional state-of-the-art (SOTA) FIQA methods and ensures stable performance in face recognition systems.