Abstract
Data scarcity and distribution shift pose major challenges for masked face detection and recognition. We propose a two-step generative data augmentation framework that combines rule-based mask warping with unpaired image-to-image translation using GANs, enabling the generation of realistic masked-face samples beyond purely synthetic transformations. Compared to rule-based warping alone, the proposed approach yields consistent qualitative improvements and complements existing GAN-based masked face generation methods such as IAMGAN. We introduce a non-mask preservation loss and stochastic noise injection to stabilize training and enhance sample diversity. Experimental observations highlight the effectiveness of the proposed components and suggest directions for future improvements in data-centric augmentation for face recognition tasks.
Abstract (translated)
数据稀缺和分布变化给戴口罩人脸检测与识别带来了重大挑战。为此，我们提出了一种两步生成式数据增强框架，该框架结合了基于规则的口罩变形技术与未配对图像到图像转换（使用GAN）的方法，能够生成超越单纯合成变换的真实戴口罩人脸样本。相较于仅采用基于规则的变形方法，所提出的方案在定性上持续改进，并补充现有的如IAMGAN等基于GAN的戴口罩人脸生成方法。我们引入了非面具保持损失和随机噪声注入来稳定训练并增强样本多样性。实验观察结果强调了所提出组件的有效性，并为未来以数据为中心的数据增强技术在人脸识别任务中的改进方向提供了建议。
URL
https://arxiv.org/abs/2512.15774
