Eyeglasses removal is challenging in removing different kinds of eyeglasses, e.g., rimless glasses, full-rim glasses, and sunglasses, and recovering appropriate eyes. Due to the significant visual variants, the conventional methods lack scalability. Most existing works focus on the frontal face images in the controlled environment, such as the laboratory, and need to design specific systems for different eyeglass types. To address the limitation, we propose a unified eyeglass removal model called the eyeglasses removal generative adversarial network (ERGAN), which could handle different types of glasses in the wild. The proposed method does not depend on the dense annotation of eyeglasses location but benefits from the large-scale face images with weak annotations. Specifically, we study the two relevant tasks simultaneously, that is, removing eyeglasses and wearing eyeglasses. Given two face images with and without eyeglasses, the proposed model learns to swap the eye area in two faces. The generation mechanism focuses on the eye area and invades the difficulty of generating a new face. In the experiment, we show the proposed method achieves a competitive removal quality in terms of realism and diversity. Furthermore, we evaluate ERGAN on several subsequent tasks, such as face verification and facial expression recognition. The experiment shows that our method could serve as a preprocessing method for these tasks.

中文翻译：

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在野外无人监督的情况下摘除眼镜


摘除眼镜对于摘除不同类型的眼镜（例如无框眼镜、全框眼镜和太阳镜）并恢复合适的眼睛来说是具有挑战性的。由于显着的视觉变化，传统方法缺乏可扩展性。现有的大多数工作都集中在受控环境（例如实验室）中的正面图像，并且需要针对不同的眼镜类型设计特定的系统。为了解决这个限制，我们提出了一种统一的眼镜去除模型，称为眼镜去除生成对抗网络（ERGAN），它可以在野外处理不同类型的眼镜。该方法不依赖于眼镜位置的密集注释，而是受益于弱注释的大规模人脸图像。具体来说，我们同时研究两个相关任务，即摘掉眼镜和戴上眼镜。给定两张戴眼镜和不戴眼镜的面部图像，所提出的模型学习交换两张脸上的眼睛区域。生成机制聚焦于眼部区域，侵入生成新面孔的难度。在实验中，我们表明所提出的方法在真实性和多样性方面实现了有竞争力的去除质量。此外，我们在几个后续任务上评估 ERGAN，例如人脸验证和面部表情识别。实验表明我们的方法可以作为这些任务的预处理方法。