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DArFace: Deformation Aware Robustness for Low Quality Face Recognition

DArFace: Deformation Aware Robustness for Low Quality Face Recognition

来源:Arxiv_logoArxiv
英文摘要

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 \textbf{DArFace}, a \textbf{D}eformation-\textbf{A}ware \textbf{r}obust \textbf{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.

Abdullah Aldahlawi Thakaa、Sadaf Gulshad

计算技术、计算机技术

Abdullah Aldahlawi Thakaa,Sadaf Gulshad.DArFace: Deformation Aware Robustness for Low Quality Face Recognition[EB/OL].(2025-07-11)[2025-07-16].https://arxiv.org/abs/2505.08423.点此复制

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