VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results
This addresses the need for efficient face image quality assessment in applications like biometrics, but it is incremental as it builds on existing challenge frameworks.
The paper tackled the problem of assessing face image quality under real-world degradations by organizing a challenge to develop lightweight models for predicting Mean Opinion Scores, resulting in 127 participants and 1519 submissions evaluated on correlation metrics.
Face images play a crucial role in numerous applications; however, real-world conditions frequently introduce degradations such as noise, blur, and compression artifacts, affecting overall image quality and hindering subsequent tasks. To address this challenge, we organized the VQualA 2025 Challenge on Face Image Quality Assessment (FIQA) as part of the ICCV 2025 Workshops. Participants created lightweight and efficient models (limited to 0.5 GFLOPs and 5 million parameters) for the prediction of Mean Opinion Scores (MOS) on face images with arbitrary resolutions and realistic degradations. Submissions underwent comprehensive evaluations through correlation metrics on a dataset of in-the-wild face images. This challenge attracted 127 participants, with 1519 final submissions. This report summarizes the methodologies and findings for advancing the development of practical FIQA approaches.