发表机构
Johannes Gutenberg University Mainz; University of Ljubljana（美因茨约翰内斯古腾堡大学; 卢布尔雅那大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究面部图像质量评估，指出常用的EDC协议存在测试集差异和阈值漂移问题，提出基于舍弃的EDC变体及秩一致性评估指标，经多数据集、模型和方法实验，证明所提方法能实现更可靠可比评估，且适用性广。
AI 中文摘要
面部图像质量评估（FIQA）旨在估计面部图像用于可靠识别的效用。FIQA方法的评估主要基于错误与舍弃特性（EDC），通过逐步舍弃低质量样本并测量保留子集上的识别错误来评估性能。本文表明广泛使用的EDC协议存在基本局限性：测试集差异和阈值漂移，这限制了FIQA方法的可靠性和可比性。为此，我们提出了基于舍弃的EDC变体和基于排序的秩一致性评估（RCE）指标，该指标在不丢弃样本的情况下对整个测试集进行操作，使用固定决策阈值。在五个数据集、四个面部识别模型和15种先进的FIQA方法上进行的大量实验证明了EDC的局限性以及所提方法在实现更可靠和可比评估方面的有效性。尽管仅对面部图像进行评估，但这些局限性源于EDC协议而非生物特征模态，表明其在一般生物特征质量评估中具有更广泛的适用性。
英文摘要
Face Image Quality Assessment (FIQA) aims to estimate the utility of facial images for reliable recognition. The evaluation of FIQA methods is predominantly based on the Error-versus-Discard Characteristic (EDC), which evaluates performance by progressively discarding low-quality samples and measuring recognition error on the retained subset. In this work, we demonstrate that the widely used EDC protocol has fundamental limitations: Test-Set Divergence and Threshold Drift, which together limit the reliability and comparability of FIQA methods. To address this, we propose discard-based EDC variants and a rank-based Rank Consistency Evaluation (RCE) metric that operates on the entire test set without discarding samples, using a fixed decision threshold. Extensive experiments on five datasets, four face recognition models, and 15 state-of-the-art FIQA methods demonstrate both the limitations of EDC and the effectiveness of the proposed approaches in enabling a more reliable and comparable evaluation. Despite evaluated on face images only, the limitations arise from the EDC protocol rather than the biometric modality, suggesting a broader applicability to biometric quality assessment in general.
