ARoFace: Alignment Robustness to Improve Low-Quality Face Recognition

TL;DR

ARoFace introduces a method to improve low-quality face recognition by making models more robust to face alignment errors through spatial transformations and adversarial training, leading to better performance on standard benchmarks.

Contribution

The paper presents a novel approach that explicitly models face alignment errors as a quality factor and enhances model robustness using differentiable transformations and adversarial augmentation.

Findings

Improved Rank1 accuracy on IJB-B and IJB-C datasets.

Enhanced robustness to face alignment errors in low-quality images.

Achieved +4.3% and +2.63% improvements on benchmark datasets.

Abstract

Aiming to enhance Face Recognition (FR) on Low-Quality (LQ) inputs, recent studies suggest incorporating synthetic LQ samples into training. Although promising, the quality factors that are considered in these works are general rather than FR-specific, \eg, atmospheric turbulence, resolution, \etc. Motivated by the observation of the vulnerability of current FR models to even small Face Alignment Errors (FAE) in LQ images, we present a simple yet effective method that considers FAE as another quality factor that is tailored to FR. We seek to improve LQ FR by enhancing FR models' robustness to FAE. To this aim, we formalize the problem as a combination of differentiable spatial transformations and adversarial data augmentation in FR. We perturb the alignment of the training samples using a controllable spatial transformation and enrich the training with samples expressing FAE. We demonstrate the benefits of the proposed method by conducting evaluations on IJB-B, IJB-C, IJB-S (+4.3\% Rank1), and TinyFace (+2.63\%). \href{https://github.com/msed-Ebrahimi/ARoFace}{https://github.com/msed-Ebrahimi/ARoFace}