A Unified Framework For Masked and Mask-Free Face Recognition Via Feature Rectification

The current state-of-the-art (SOTA) methods validate the role of shape in object categorization, however, except few, most of them neglect object shape information. Motivated by low-shot learning and increasing synthetic data in vision tasks, we investigated how image-based embedding generalization can be improved by the data itself. We propose a new data augmentation approach for low-shot object generalization regime based on image-only. The proposed method learns a discriminative embedding space using SIFT shape points for 3D objects, such that it's easier to map images and point clouds into one. Numerous experiments show that the proposed approach is superior to the existing low-shot SOTA methods.