AMSFANet: attention-based multiscale small face aware restoration method

Deep learning has achieved remarkable performance in various fields, including face recognition. However, recognizing small-sized face images remains a challenge in this domain. The limited number of pixels in small face images makes it difficult to extract facial features, leading to decreased accuracy of face recognition systems. Furthermore, small face images often suffer from low resolution and poor image quality, which further complicates the recognition process. To address this issue, this paper proposes a novel method for low-resolution face restoration by transforming it into a mapping problem from low-resolution small face images to high-resolution face images. We introduce an attention-based multiscale small face aware network (AMSFANet) for low-resolution face restoration. The proposed method is based on a super-resolution generative adversarial network (SRGAN) with improved loss constraints using the wasserstein distance and gradient penalty strategy to enhance the model’s robustness during training. We also propose an attention-based multiscale residual module to replace the traditional residual structure, which strengthens the generator’s focus on faces, reduces the impact of complex backgrounds on face restoration, and improves the final image’s facial clarity, making it effective for subsequent face recognition. Experimental results demonstrate that the proposed method effectively improves the quality of low-resolution face images and enhances subsequent face recognition accuracy.