Uncertainty-Aware Face Embedding with Contrastive Learning for Open-Set Evaluation

Abstract

While advances in deep learning have enabled novel applications in various fields, face recognition in open-set scenarios remains a complex task, owing to the challenges posed by the extensive volume of low-quality face images. We introduce a new approach for recognizing faces in unconstrained open-set settings by leveraging uncertainty-aware embeddings through contrastive learning. Our model, called UCFace, effectively regulates the contribution of each face image based on the <italic>face uncertainty</italic> derived from image quality as an inverse proxy. Face embeddings are reinterpreted as a probabilistic distribution within the embedding space, where the degree of sharpness (i.e., distribution concentration) reflects the underlying uncertainty and probability density is used as a similarity metric to facilitate contrastive learning. Experiments on a wide range of face datasets, including those with high, mixed, and real-world low-resolution face images, demonstrate that UCFace enhances open-set face recognition performance by integrating the aspect of uncertainty. IEEE