  In this paper, we tackle the challenge of face recognition in the wild, where
images often suffer from low quality and real-world distortions. Traditional
heuristic approaches-either training models directly on these degraded images
or their enhanced counterparts using face restoration techniques-have proven
ineffective, primarily due to the degradation of facial features and the
discrepancy in image domains. To overcome these issues, we propose an effective
adapter for augmenting existing face recognition models trained on high-quality
facial datasets. The key of our adapter is to process both the unrefined and
enhanced images using two similar structures, one fixed and the other
trainable. Such design can confer two benefits. First, the dual-input system
minimizes the domain gap while providing varied perspectives for the face
recognition model, where the enhanced image can be regarded as a complex
non-linear transformation of the original one by the restoration model. Second,
both two similar structures can be initialized by the pre-trained models
without dropping the past knowledge. The extensive experiments in zero-shot
settings show the effectiveness of our method by surpassing baselines of about
3%, 4%, and 7% in three datasets. Our code will be publicly available.
