Abstract
Facial recognition systems have achieved remarkable success by leveraging deep neural networks, advanced loss functions, and large-scale datasets. However, their performance often deteriorates in real-world scenarios involving low-quality facial images. Such degradations, common in surveillance footage or standoff imaging include low resolution, motion blur, and various distortions, resulting in a substantial domain gap from the high-quality data typically used during training. While existing approaches attempt to address robustness by modifying network architectures or modeling global spatial transformations, they frequently overlook local, non-rigid deformations that are inherently present in real-world settings. In this work, we introduce DArFace, a Deformation-Aware robust Face recognition framework that enhances robustness to such degradations without requiring paired high- and low-quality training samples. Our method adversarially integrates both global transformations (e.g., rotation, translation) and local elastic deformations during training to simulate realistic low-quality conditions. Moreover, we introduce a contrastive objective to enforce identity consistency across different deformed views. Extensive evaluations on low-quality benchmarks including TinyFace, IJB-B, and IJB-C demonstrate that DArFace surpasses state-of-the-art methods, with significant gains attributed to the inclusion of local deformation modeling.
Abstract (translated)
面部识别系统通过利用深度神经网络、先进的损失函数和大规模数据集取得了显著的成功。然而，在涉及低质量面部图像的现实场景中，例如监控录像或远距离成像中的低分辨率、运动模糊以及各种失真，这些系统的性能往往会出现下降。这些问题导致了训练时通常使用的高质量数据与实际应用中存在的低质量数据之间存在着较大的领域差距（domain gap）。 虽然现有方法试图通过修改网络架构或者模拟全局空间变换来提高系统的鲁棒性，但它们常常忽视了在现实环境中普遍存在且固有的局部非刚体变形。为此，在这项工作中，我们引入了一种名为DArFace的“感知变形”的面部识别框架，该框架能够在不使用高质量和低质量配对训练样本的情况下提升系统对抗这些退化的性能。 我们的方法通过在训练过程中模拟全局变换（如旋转、平移）以及局部弹性变形来敌对性地集成两种类型的变形，从而模拟实际的低质量情况。此外，我们引入了一个对比目标以确保不同变形视图之间的身份一致性。 我们在包括TinyFace、IJB-B和IJB-C在内的多个低质量基准数据集上进行了广泛的评估，结果显示DArFace超越了现有的最佳方法，并且显著的性能提升主要归因于局部变形建模的加入。
URL
https://arxiv.org/abs/2505.08423
