Face recognition models often encounter challenges while recognizing partially occluded faces. Disguise can be manifested intentionally to impersonate someone or unintentionally when the subject wears artifacts such as sunglasses, masks, hats, and caps. To identify a subject accurately, it is essential to discard the occluded regions of the subject's face and use the features extracted from the visible regions. The problem is further exacerbated when the input image is low resolution or captured at a distance. This article proposes a novel Segmentation based Disguise Guided Dropout Network (Seg-DGDNet) to identify the occluded facial features and recognize a person by non-occluded biometric features. The proposed Seg-DGDNet has two primary tasks: 1) identifying the non-occluded pixels in the subject's face using segmentation models and 2) guiding the recognition model to concentrate on visible facial features with the help of the proposed guided dropout. The performance of the proposed model is evaluated on three disguised face datasets with artifacts such as facial masks and sunglasses. The proposed model outperforms existing state-of-the-art face recognition models by a significant margin on different datasets with various levels of disguise and resolutions.

中文翻译：

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Seg-DGDNet：用于低分辨率人脸识别的基于分割的伪装引导丢弃网络

人脸识别模型在识别部分遮挡的人脸时经常遇到挑战。伪装可以是有意地冒充某人，也可以是无意地当对象戴着太阳镜、面具、帽子和鸭舌帽等物品时。为了准确地识别对象，必须丢弃对象面部的遮挡区域并使用从可见区域提取的特征。当输入图像分辨率较低或远距离拍摄时，该问题会进一步恶化。本文提出了一种新颖的基于分割的伪装引导丢弃网络（Seg-DGDNet）来识别遮挡的面部特征并通过非遮挡的生物特征识别人。所提出的 Seg-DGDNet 有两个主要任务：1）使用分割模型识别主体面部中的非遮挡像素，2）借助所提出的引导 dropout 引导识别模型集中于可见的面部特征。所提出模型的性能在三个带有面具和太阳镜等伪影的伪装面部数据集上进行评估。所提出的模型在具有不同伪装和分辨率级别的不同数据集上明显优于现有最先进的人脸识别模型。