Perturbation-insensitive cross-domain image enhancement for low-quality face verification

Low-quality face images present two typical problems: perceptual distortion and a significant reduction in verification accuracy. Prevailing works address these two problems by face image enhancement. Generally, they require low- and high-quality image pairs or facial priors, e.g., facial landmarks, which are relatively rare in real-world scenarios. In this paper, we introduce a fresh perspective to address these problems, by hypothesizing that there is a positive correlation between cross-domain image enhancement and adversarial defense. With the incorporation of cross-domain image enhancement, paired images or facial priors are no longer necessary. Furthermore, we introduce two types of adversarial perturbations, namely appearance and semantic perturbations, and defending these perturbations aims to solve the aforementioned two typical problems. Defending appearance perturbations decreases perceptual distortion and improve image quality, while defending semantic perturbations promotes identity preservation during the image enhancement process, which improves verification accuracy. To this end, we propose a collaborative face enhancement module (COFEM) for face verification based on two types of adversarial perturbation examples. COFEM incorporates three components. First, an adversarial example generator attacks high-quality images (source domain) in two different ways to obtain appearance and semantic perturbation examples. Next, an image enhancement network denoises these perturbation examples and enhances the quality of low-quality images (target domain). Then, an image reconstruction network is utilized to preserve the identity of the enhanced image such that it is consistent with that of the corresponding input. Unlike prevailing image enhancement models which mainly focus on high perceptual quality, COFEM emphasizes identity-related feature preservation, which is vital to face verification. Combined with COFEM, we also design a face verification module to form a low-quality face verification approach. Extensive experiments demonstrate the effectiveness of our approach in improving the low-quality face image quality and verification accuracy.