PId-SAGAN: A novel Generative Self-Attention with Identity Preservation for Person ReID in Uncontrolled Environments

Person re-identification (ReID) is a core task in computer vision, requiring consistent identification of individuals across cameras despite variations in appearance, pose, lighting, and occlusion. Although deep learning methods have significantly improved performance, they remain sensitive to degraded image quality, dependence on large annotated datasets, and limited generalization under uncontrolled conditions. In this paper, we propose PId-SAGAN, a generative self-attention framework with identity preservation for person ReID. We introduce Final-Layer Identity Attention (FLIA), a lightweight self-attention mechanism applied exclusively at the generator’s final layer. This design enables the model to capture long-range dependencies, refine global structures, suppress background noise, and preserve fine-grained identity cues through an identity-preserving loss, while maintaining computational efficiency. The proposed framework integrates identity-aware image generation with CNN-based ReID learning using both real and synthetic data, improving robustness under challenging scenarios such as occlusion, illumination changes, and pose variations. Experimental results on multiple public benchmarks demonstrate that PId-SAGAN achieves competitive performance compared to recent generative and attention-based approaches in uncontrolled real-world person ReID.