稳定学习：累积相对点边际分数用于人脸图像质量评估
Learning Steadily: Accumulating Relative Point Margin Scores for Face Image Quality Assessment
AI总结：
针对FR集成FIQA训练不稳定问题，提出CARPM-FIQA累积相对点边际分数，降低方差、提升排序稳定性，在多个基准上取得前列性能。
AI中文摘要：
人脸图像质量评估（FIQA）用于确定所采集的人脸图像是否适合自动化人脸识别（FR），这是可靠生物识别系统的关键能力。现有的最先进的FR集成FIQA方法存在时间不稳定性问题：随着训练过程中特征空间的演变，单轮次的质量估计会波动，从而形成一个移动目标，削弱了可靠质量预测的基础。我们提出了CARPM-FIQA，一种针对FR集成FIQA的稳定化策略，该策略在整个训练轨迹中累积相对点边际测量值（类内紧凑性与类间分离度之比），而非依赖单轮次估计。这种累积平均方法提供了理论上的优势：降低了质量估计的方差，改善了均方误差，并随着训练进展增强了排序稳定性且具有收敛保证。通过在带有标注质量分组的SynFIQA数据集上的受控实验，我们证明了累积平均方法具有更优的判别能力，且在不同训练配置下的消融研究确认了其一致的改进效果。在八个具有挑战性的基准上，使用四种FR模型在两个FMR阈值下，与十二种FIQA方法进行比较，CARPM-FIQA在17种比较方法中，按跨FR模型平均的pAUC-EDC和AUC-EDC以及跨基准归一化后的平均值，分别排名第4（CARPM-FIQA(L)）和第6（CARPM-FIQA(S)），且对于每种FR模型，其归一化平均值与最佳方法的差距均在几个百分点以内，为训练不稳定性问题提供了一种原则性解决方案，同时保持了FR集成的性能优势。更广泛地说，我们的工作表明，在目标值因特征表示演变而固有波动的深度学习系统中，时间聚合策略可以稳定训练目标。
英文摘要：
Face Image Quality Assessment determines the suitability of captured face images for automated face recognition (FR), a critical capability for reliable biometric systems. Existing state-of-the-art FR-integrated FIQA methods suffer from temporal instability: as the feature space evolves during training, single-epoch quality estimates fluctuate, creating a moving target that undermines reliable quality prediction. We introduce CARPM-FIQA, a stabilization strategy for FR-integrated FIQA that accumulates relative point margin measurements, the ratio between intra-class compactness and inter-class separation, across the entire training trajectory rather than relying on single-epoch estimates. This cumulative averaging approach provides theoretically grounded advantages: reduced variance in quality estimates, improved mean squared error, and enhanced ranking stability with convergence guarantees as training progresses. Through controlled experiments on the SynFIQA dataset with labeled quality groups, we demonstrate that cumulative averaging achieves superior discriminative ability, and ablation studies across different training configurations confirm consistent improvements. Evaluated against twelve FIQA methods on eight challenging benchmarks with four FR models at two FMR thresholds, CARPM-FIQA places 4th (CARPM-FIQA(L)) and 6th (CARPM-FIQA(S)) of 17 compared methods by pAUC-EDC and AUC-EDC averaged across FR models and, after per-benchmark normalization, across benchmarks, staying within a few percent of the best method's normalized average for every FR model, providing a principled solution to training instability while maintaining the performance benefits of FR integration. More broadly, our work demonstrates that temporal aggregation strategies can stabilize training objectives in deep learning systems where target values inherently fluctuate due to evolving feature representations.
