Abstract
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.
Abstract (translated)
人脸识别（FR）在许多关键应用中扮演着重要角色，识别过程中的错误可能导致严重后果。人脸图像质量评估（FIQA）技术通过提供面部样本的质量估计来增强人脸识别系统，使得系统能够丢弃不适合可靠识别或导致低置信度识别决策的样本。 大多数最新的FIQA技术依赖于广泛的监督训练以实现准确的质量估算。相比之下，无监督技术消除了对额外训练的需求，但往往运行较慢且通常表现出较低性能。在本文中，我们介绍了一种半监督、无需训练的方法FROQ（Face Recognition Observer of Quality），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的质量，并结合了基于监督FIQA模型的效率和无监督方法的免训特性。 通过基于伪质量标签的简单校准步骤，FROQ能够揭示任何现代人脸识别模型中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颖无监督FIQA技术。在四个最先进的FR模型和八个基准数据集上进行的全面实验表明，与现有方法相比，FROQ可以实现高度竞争的结果，在不需显式训练的情况下同时提供强大的性能和高效的运行时间。
URL
https://arxiv.org/abs/2509.17689
