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2025-09-24 更新
FROQ: Observing Face Recognition Models for Efficient Quality Assessment
Authors:Žiga Babnik, Deepak Kumar Jain, Peter Peer, Vitomir Štruc
Face Recognition (FR) plays a crucial role in many critical (high-stakes) applications, where errors in the recognition process can lead to serious consequences. Face Image Quality Assessment (FIQA) techniques enhance FR systems by providing quality estimates of face samples, enabling the systems to discard samples that are unsuitable for reliable recognition or lead to low-confidence recognition decisions. Most state-of-the-art FIQA techniques rely on extensive supervised training to achieve accurate quality estimation. In contrast, unsupervised techniques eliminate the need for additional training but tend to be slower and typically exhibit lower performance. In this paper, we introduce FROQ (Face Recognition Observer of Quality), a semi-supervised, training-free approach that leverages specific intermediate representations within a given FR model to estimate face-image quality, and combines the efficiency of supervised FIQA models with the training-free approach of unsupervised methods. A simple calibration step based on pseudo-quality labels allows FROQ to uncover specific representations, useful for quality assessment, in any modern FR model. To generate these pseudo-labels, we propose a novel unsupervised FIQA technique based on sample perturbations. Comprehensive experiments with four state-of-the-art FR models and eight benchmark datasets show that FROQ leads to highly competitive results compared to the state-of-the-art, achieving both strong performance and efficient runtime, without requiring explicit training.
人脸识别（FR）在许多关键（高风险）应用中扮演着至关重要的角色，识别过程中的错误可能会导致严重的后果。人脸图像质量评估（FIQA）技术通过提供人脸样本的质量估计，增强了FR系统的性能，使系统能够丢弃那些不适合可靠识别或导致低置信度识别决策的样本。大多数最先进的FIQA技术依赖于广泛的监督训练来实现准确的质量估计。相比之下，无监督技术消除了对额外训练的需求，但往往速度较慢，通常表现较差。在本文中，我们介绍了FROQ（人脸识别质量观察者），这是一种半监督、无需训练的方法，它利用给定FR模型中的特定中间表示来估计人脸图像质量，结合了监督FIQA模型的效率和无监督方法的无需训练的方法。基于伪质量标签的简单校准步骤允许FROQ在任何现代FR模型中揭示对质量评估有用的特定表示。为了生成这些伪标签，我们提出了一种基于样本扰动的新型无监督FIQA技术。使用四种最先进的FR模型和八个基准数据集进行的综合实验表明，FROQ与最新技术相比取得了极具竞争力的结果，在强大性能和高效运行时间方面取得了成就，而无需明确的训练。
论文及项目相关链接
PDF Presented at the International Joint Conference on Biometrics (IJCB 2025)
Summary
本文主要介绍了一种名为FROQ的半监督、无需训练的人脸图像质量评估方法。该方法利用给定人脸识别模型中的特定中间表示来估计人脸图像质量，结合了监督型和非监督型图像质量评估模型的优点。通过基于伪质量标签的简单校准步骤，FROQ能够在任何现代人脸识别模型中发掘出用于质量评估的特定表示。实验表明，FROQ在多个前沿人脸识别模型和基准测试集上的表现均极具竞争力。
Key Takeaways
- FROQ是一种半监督、无需训练的人脸识别图像质量评估方法。
- FROQ利用人脸识别模型中的特定中间表示进行质量评估。
- FROQ结合了监督型和非监督型FIQA模型的优点。
- 通过基于伪质量标签的校准步骤，FROQ可在任何现代人脸识别模型中发掘质量评估的特定表示。
- FROQ的伪质量标签生成采用了一种新型的无监督FIQA技术，基于样本扰动。
- 实验表明，FROQ在多个前沿人脸识别模型和基准测试集上的表现均优异。
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