Realistic Face Super-Resolution via Generative Adversarial Networks: Enhancing Facial Recognition in Real-world Scenarios

The accuracy of real-world facial recognition operations faces challenges because of the difficulties connected to Low-Resolution image quality. Thisindicates that super-resolution methods play a vital role inimproving recognition outcomes. Currently,available SR techniques do not achieve generalization due to their dependence on synthetic LR data that uses basic down samplingprocesses. The proposed GAN-based approach establishes a solution to this challenge through its simulation of actual degradation algorithms which combine Gaussian blur with noise addition and color modification and JPEG compression. Random application of augmentation parameters allows the GAN model to acquire knowledge about diverse and realistic low-resolution data distribution patterns during training. A unique unaligned face image pair dataset was made specifically for research using Zoom-In and Zoom-Out methods to capture high-resolution and low-resolution images from the same individuals. The dataset presents authentic real-life scenarios better than conventional paired collection methods. Based on experimental results our method produces substantial gains in performance compared to other super-resolution methods across both self-created face data as well as established surveillance data. The proposed model achieves higher visual quality standards while improving facial recognition accuracy under different operational situations. In conclusion, this study implements an effective SR solution for facial recognition which tackles problems with standard training datasets while creating authentic face image data. The proposed method shows promise for enhancing SR applications which need high-quality facial recognition capability in surveillance systems and other security-based operations.