DSL-FIQA: Assessing Facial Image Quality via Dual-Set Degradation Learning and Landmark-Guided Transformer

TL;DR

This paper introduces a transformer-based face image quality assessment method that uses dual degradation learning and landmark guidance, along with a new diverse dataset, to improve robustness and generalizability.

Contribution

It proposes a novel dual-set degradation learning mechanism and landmark-guided transformer for more accurate face image quality assessment, along with a balanced large-scale dataset.

Findings

Significant improvement over prior GFIQA methods.

Robustness to real-world degradations demonstrated.

Enhanced fairness with diverse dataset design.

Abstract

Generic Face Image Quality Assessment (GFIQA) evaluates the perceptual quality of facial images, which is crucial in improving image restoration algorithms and selecting high-quality face images for downstream tasks. We present a novel transformer-based method for GFIQA, which is aided by two unique mechanisms. First, a Dual-Set Degradation Representation Learning (DSL) mechanism uses facial images with both synthetic and real degradations to decouple degradation from content, ensuring generalizability to real-world scenarios. This self-supervised method learns degradation features on a global scale, providing a robust alternative to conventional methods that use local patch information in degradation learning. Second, our transformer leverages facial landmarks to emphasize visually salient parts of a face image in evaluating its perceptual quality. We also introduce a balanced and diverse Comprehensive Generic Face IQA (CGFIQA-40k) dataset of 40K images carefully designed to overcome the biases, in particular the imbalances in skin tone and gender representation, in existing datasets. Extensive analysis and evaluation demonstrate the robustness of our method, marking a significant improvement over prior methods.