Abstract
Face recognition in unconstrained environments such as surveillance, video, and web imagery must contend with extreme variation in pose, blur, illumination, and occlusion, where conventional visual quality metrics fail to predict whether inputs are truly recognizable to the deployed encoder. Existing FIQA methods typically rely on visual heuristics, curated annotations, or computationally intensive generative pipelines, leaving their predictions detached from the encoder's decision geometry. We introduce TransFIRA (Transfer Learning for Face Image Recognizability Assessment), a lightweight and annotation-free framework that grounds recognizability directly in embedding space. TransFIRA delivers three advances: (i) a definition of recognizability via class-center similarity (CCS) and class-center angular separation (CCAS), yielding the first natural, decision-boundary--aligned criterion for filtering and weighting; (ii) a recognizability-informed aggregation strategy that achieves state-of-the-art verification accuracy on BRIAR and IJB-C while nearly doubling correlation with true recognizability, all without external labels, heuristics, or backbone-specific training; and (iii) new extensions beyond faces, including encoder-grounded explainability that reveals how degradations and subject-specific factors affect recognizability, and the first recognizability-aware body recognition assessment. Experiments confirm state-of-the-art results on faces, strong performance on body recognition, and robustness under cross-dataset shifts. Together, these contributions establish TransFIRA as a unified, geometry-driven framework for recognizability assessment -- encoder-specific, accurate, interpretable, and extensible across modalities -- significantly advancing FIQA in accuracy, explainability, and scope.
Abstract (translated)
在不受限制的环境中，如监控、视频和网络图像中的面部识别，必须应对姿态变化、模糊度、光照变化以及遮挡等极端情况。传统视觉质量指标无法预测输入是否对部署的编码器来说真正具有可识性。现有的FIQA（Face Image Recognizability Assessment）方法通常依赖于视觉直觉、手工标注或计算密集型生成流程，导致其预测结果与编码器决策几何无关。 我们提出了一种名为TransFIRA（Transfer Learning for Face Image Recognizability Assessment）的轻量级无标注框架，该框架直接在嵌入空间中定义可识性。TransFIRA实现了三项创新： (i) 通过类中心相似度（CCS）和类中心角度分离（CCAS），为过滤和加权提供了第一个自然且与决策边界对齐的标准。 (ii) 提出了一种基于可识别性的聚合策略，该策略在BRIAR和IJB-C数据集上实现了最先进的验证准确率，并将与真实可识性相关的相关性几乎翻倍。这些结果均无需外部标签、直觉或针对特定骨干网络的训练。 (iii) TransFIRA扩展了面部以外的应用，包括基于编码器解释性的方法，揭示了退化和主体特有因素如何影响可识别性，并提出了首个具有可识性意识的身体识别评估。 实验表明，在面部数据集中TransFIRA达到了最先进的结果，在身体识别方面表现出色，并且在跨数据集迁移时也表现稳健。这些贡献共同确立了TransFIRA作为统一、几何驱动的框架，用于可识别性评估：针对特定编码器，准确，可解释，且具有跨模式扩展的能力。这显著提高了FIQA的准确性、可解释性和应用范围。
URL
https://arxiv.org/abs/2510.06353
