Abstract
Face Recognition (FR) tasks have made significant progress with the advent of Deep Neural Networks, particularly through margin-based triplet losses that embed facial images into high-dimensional feature spaces. During training, these contrastive losses focus exclusively on identity information as labels. However, we observe a multiscale geometric structure emerging in the embedding space, influenced by interpretable facial (e.g., hair color) and image attributes (e.g., contrast). We propose a geometric approach to describe the dependence or invariance of FR models to these attributes and introduce a physics-inspired alignment metric. We evaluate the proposed metric on controlled, simplified models and widely used FR models fine-tuned with synthetic data for targeted attribute augmentation. Our findings reveal that the models exhibit varying degrees of invariance across different attributes, providing insight into their strengths and weaknesses and enabling deeper interpretability. Code available here: this https URL}{this https URL
Abstract (translated)
面部识别（FR）任务随着深度神经网络的出现取得了显著进展，特别是通过基于边距的三元组损失将面部图像嵌入到高维特征空间中。在训练过程中，这些对比损失仅关注身份信息作为标签。然而，我们观察到，在嵌入空间中出现了由可解释的面部（如发色）和图像属性（如对比度）影响的多尺度几何结构。我们提出了一种基于几何的方法来描述FR模型对这些属性的依赖或不变性，并引入了一个受物理学启发的对齐指标。我们在控制下的简化模型和广泛使用的FR模型上评估了该指标，后者使用合成数据进行了微调以增强特定属性。我们的研究结果表明，不同属性下模型表现出不同程度的不变性，这为理解它们的优势与劣势提供了洞见，并促进了更深层次的理解。代码可在以下链接获取：[此处插入具体URL]
URL
https://arxiv.org/abs/2507.11372
