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.

本研究提出一种新的生成模型，利用其构建一个双模态互斥 GAN 模型，通过对两个组成部分整体损失和局部损失的衡量，来重构被遮挡住的面部，该模型通过对实际和合成数据组进行消融实验，显示出高于其他竞争方法的方法，可大幅提高人脸识别的性能。

SD-GAN: 结构性去噪 GAN 揭示遮挡下的面部部位