Low-Resolution Face Recognition Enhanced by High-Resolution Facial Images

Abstract

Despite recent advances in high-resolution (HR) face recognition, recognizing identities from low-resolution (LR) facial images remains challenging due to the absence of facial shape and detail. Current research focuses solely on reducing the distribution discrepancy between the HR and LR embeddings from the output layer, rather than thoroughly investigating the superiority of HR facial images for improved performance. In this paper, we propose a novel low-resolution face recognition method enhanced by the guidance of high-resolution facial images in both feature map space and embedding space. Specifically, in feature map space, the similarity constraint across the multilayer feature maps is adopted to align the intermediate features of facial images. Then we introduce multiple generators to recover HR images from extracted feature maps and utilize the reconstructed loss to supplement the missing facial details in LR images. In embedding space, we propose a supervised auxiliary contrastive loss to encourage the paired HR and LR embedding from the same class to be pulled together, whereas those from different classes are pushed apart. The one-to-many matching strategy and the adaptive weight adjustment strategy are applied to make the network adapt to the inputs of different resolutions. Experiments on four benchmark datasets with both synthesized and realistic LR facial images demonstrate the superiority of the proposed method to state-of-the-art.