Generative Adversarial Networks (GAN) based Person Re-Identification : A Review

Person re-identification (Re-ID) represents a critical area within computer vision where the goal is matching individuals across non-overlapping camera views. It has recently drawn a lot of attention considering its numerous applications in surveillance, smart cities, and forensic analysis. The traditional Re-ID methods relied much on hand-crafted features and metric learning that have often struggled with scalability, pose variations, and domain shifts. The emergence of generative models, especially Generative Adversarial Networks (GANs), has dramatically changed the Re-ID landscape by making it possible to generate realistic data synthesis, domain adaptation, and feature representation improvement. This paper presents a review of GAN-based methods for person Re-ID, focusing on working principles, notable studies, statistical analysis of prominent datasets, and evaluation metrics. GANs limitations and potential for future innovation have been discussed, while covering how they help overcome issues such as data scarcity and domain generalization. Finally, a future direction bridging the theoretical advancements to real-world applicability is shown for GAN-based Re-ID systems.