MSPT: A Lightweight Face Image Quality Assessment Method with Multi-stage Progressive Training

TL;DR

This paper introduces MSPT, a lightweight face image quality assessment network with a multi-stage progressive training strategy that enhances performance while maintaining efficiency, suitable for practical deployment.

Contribution

The paper proposes a novel multi-stage progressive training approach for lightweight face quality assessment networks, improving learning of complex features and reducing catastrophic forgetting.

Findings

Achieved second highest score on VQualA 2025 benchmark

Comparable or better performance than state-of-the-art methods

Maintains efficient inference with a lightweight model

Abstract

Accurately assessing the perceptual quality of face images is crucial, especially with the rapid progress in face restoration and generation. Traditional quality assessment methods often struggle with the unique characteristics of face images, limiting their generalizability. While learning-based approaches demonstrate superior performance due to their strong fitting capabilities, their high complexity typically incurs significant computational and storage costs, hindering practical deployment. To address this, we propose a lightweight face quality assessment network with Multi-Stage Progressive Training (MSPT). Our network employs a three-stage progressive training strategy that gradually introduces more diverse data samples and increases input image resolution. This novel approach enables lightweight networks to achieve high performance by effectively learning complex quality features while significantly mitigating catastrophic forgetting. Our MSPT achieved the second highest score on the VQualA 2025 face image quality assessment benchmark dataset, demonstrating that MSPT achieves comparable or better performance than state-of-the-art methods while maintaining efficient inference.