Improving Person-Re Identification with Dual-Generator and Dual-Discriminator Architecture in Conditional GANs

A significant challenge in computer vision, which aims to recognise a person across numerous camera views, is the re-identification of persons over several non-interconnected camera views. Generative Adversarial Networks (GANs) have been employed successfully in person re-identification tasks, where they can generate realistic images of a person based on a query image. In this paper, a new novel Dual-Generator and Dual-Discriminator architecture for Conditional GANs is used for re-identification. The proposed architecture consists of two image generators and two discriminators that generate high-quality images and capture a person's identity information efficiently. The discriminators use the Wasserstein distance method for classification between real and fake images. Additionally, the system utilizes low-light image optimization for pre-processing the images, which improves the quality and visibility of the images captured in low-light conditions, making them more suitable for re-identification. Study results show that the Dual-Generator and Dual-Discriminator architecture for Conditional GAN re-identification achieves improved accuracy and resilience. This research paper presents a new cutting-edge method for person-re identification.