Face recognition plays the significant role in many human-computer interaction decvices and applications, whose access control systems are based on the verification of face biometrical features. Though great improvement in the recognition performances have been achieved, when under some specific conditions like faces with occlusions, the performance would suffer a severe drop. Occlusion is one of the most significant reasons for the performance degrade of the existing general face recognition systems. The biggest problem in occluded face recognition (OFR) lies in the lack of the occluded face data. To mitigate this problem, this paper has proposed one new OFR network DOMG-OFR (Dynamic Occlusion Mask Generator based Occluded Face Recognition), which keeps trying to generate the most informative occluded face training samples on feature level dynamically, in this way, the recognition model would always be fed with the most valuable training samples so as to save the labor in preparing the synthetic data while simultaneously improving the training efficiency. Besides, this paper also proposes one new module called Decision Module (DM) in an attempt to combine both the merits of the two mainstream methodologies in OFR which are face image reconstruction based methodologies and the face feature filtering based methodologies. Furthermore, to enable the existing face deocclusion methods that mostly target at near frontal faces to work well on faces under large poses, one head pose aware deocclusion pipeline based on the Condition Generative Adversarial Network (CGAN) is proposed. In the experimental parts, we have also investigated the effects of the occlusions upon face recognition performance, and the validity and the efficiency of our proposed Decision based OFR pipeline has been fully proved. Through comparing both the verification and the recognition performance upon both the real occluded face datasets and the synthetic occluded face datasets with other existing works, our proposed OFR architecture has demonstrated obvious advantages over other works.

人脸识别在许多人机交互设备和应用中发挥着重要作用，其门禁系统基于人脸生物特征的验证。虽然识别性能取得了很大的进步，但在某些特定条件下，例如人脸被遮挡时，性能会严重下降。遮挡是现有通用人脸识别系统性能下降的最重要原因之一。遮挡人脸识别（OFR）最大的问题在于缺乏遮挡人脸数据。为了缓解这一问题，本文提出了一种新的 OFR 网络 DOMG-OFR（基于动态遮挡掩模生成器的遮挡人脸识别），该网络不断尝试动态生成特征级别上信息最丰富的遮挡人脸训练样本，从而实现识别模型始终会被输入最有价值的训练样本，以节省准备合成数据的劳动力，同时提高训练效率。此外，本文还提出了一种称为决策模块（DM）的新模块，试图结合OFR中两种主流方法（基于人脸图像重建的方法和基于人脸特征过滤的方法）的优点。此外，为了使现有的主要针对近正面的面部去遮挡方法能够在大姿势下的面部上很好地工作，提出了一种基于条件生成对抗网络（CGAN）的头部姿势感知去遮挡管道。 在实验部分，我们还研究了遮挡对人脸识别性能的影响，并且我们提出的基于决策的 OFR 管道的有效性和效率得到了充分证明。通过将真实遮挡人脸数据集和合成遮挡人脸数据集的验证和识别性能与其他现有工作进行比较，我们提出的 OFR 架构比其他工作表现出明显的优势。