Seg-DGDNet: Segmentation based Disguise Guided Dropout Network for Low Resolution Face Recognition

Face recognition models often face 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 paper 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.