论文标题
SD-GAN：结构和脱糖的gan在遮挡下揭示了面部零件
SD-GAN: Structural and Denoising GAN reveals facial parts under occlusion
论文作者
论文摘要
某些面部零件的外观显着（独特），这极大地有助于对受试者的整体认识。这些显着部分的阻塞会恶化面部识别算法的性能。在本文中，我们提出了一个生成模型，以重建面部缺失的部分，这些部分在遮挡下。提出的生成模型（SD-GAN）重建了一个表面，以保留面部的照明变化和身份。一种新型的对抗训练算法是为双峰互斥生成对抗网络（GAN）模型设计的，用于更快地收敛。还提出了一种新型的对抗性“结构”损失函数，包括两个组成部分：整体和局部损失，其特征是SSIM和斑块的MSE。关于真实和合成的面部数据集的消融研究表明，即使是为了提高面部识别的性能，我们提出的技术也比相当大的差距优于竞争方法。
Certain facial parts are salient (unique) in appearance, which substantially contribute to the holistic recognition of a subject. Occlusion of these salient parts deteriorates the performance of face recognition algorithms. In this paper, we propose a generative model to reconstruct the missing parts of the face which are under occlusion. The proposed generative model (SD-GAN) reconstructs a face preserving the illumination variation and identity of the face. A novel adversarial training algorithm has been designed for a bimodal mutually exclusive Generative Adversarial Network (GAN) model, for faster convergence. A novel adversarial "structural" loss function is also proposed, comprising of two components: a holistic and a local loss, characterized by SSIM and patch-wise MSE. Ablation studies on real and synthetically occluded face datasets reveal that our proposed technique outperforms the competing methods by a considerable margin, even for boosting the performance of Face Recognition.
