Few-Shot Face Recognition: Leveraging GAN for Effective Data Augmentation

Face recognition technology is a prominent research area in the digital age, with significant applications in commerce and security. This technology relies on high-quality training data, which poses a challenge in practical engineering applications owing to the substantial investment required and the stringent privacy protection regulations that aggravate the problem of sample scarcity, resulting in few-shot learning conditions. The collection of face data under specific conditions, such as extreme lighting and poses significant challenges. Furthermore, the imbalance in sample distribution severely impacts the model’s ability to generalize and achieve accurate recognition. This paper addresses this issue by leveraging Generative Adversarial Networks (GANs) for effective data augmentation. We propose using the architectures of SR-StarGAN and FPNSA-AttGAN to generate diverse virtual face images in different feature domains, constructing a large-scale, widely distributed dataset to support face recognition under various attributes and complex conditions, enabling effective few-shot face recognition. We detail the core algorithms and network frameworks of SR-StarGAN and FPNSA-AttGAN, and demonstrate the training process of IdentiFace using the synthetic samples. The results demonstrate a significant enhancement in face recognition accuracy, from 83.59% to 96.64%, providing a viable approach to address data scarcity, achieving enhanced generalization capability under data-constrained few-shot learning scenarios and offering valuable insights for future studies in generative-based face recognition.