Identity-Preserved Facial Diversity With Gans and Recognition Via Fewshot Approach

Face recognition technologies, integral to security, surveillance, and biometric identification, are continually advancing, yet the accurate detection and recognition of individuals with evolving facial features due to aging, lifestyle changes, medical procedures, and other factors remained a challenging task. This research embarked on a methodical exploration to address this issue by utilizing Generative Adversarial Networks, specifically the StyleGAN3 model. This study navigated the intricate process of manipulating both local and global facial attributes in synthetic images, creating a rich facial diversity while maintaining the consistency of individual identity. To complement this, this research comprehensively studied the capabilities of state-of-the-art Yolo models through few shots approaches, mimicking real world data paucity. This research underscores the transformative potential of GANs, specifically the StyleGAN3 model, in the constantly evolving domain of facial recognition. The encouraging outcomes from the Yolo models, particularly when furnished with limited data, not only challenge traditional training paradigms but also illuminate the significance of adept synthetic data generation techniques. Such advancements hold potential to augment security, surveillance, and streamline biometric identification processes.