IQAGA: Image Quality Assessment-Driven Learning with GAN-Based Dataset Augmentation for Cross-Domain Person Re-Identification

Person re-identification (reID) is the task of matching images of the same person across different cameras or domains. It has many applications in security, surveillance, and biometrics. However, supervised learning-based person reID faces the challenge of domain shift, which means that the performance of a model trained on a specific domain (source domain) may degrade when testing on another domain (target domain) with different distributions, backgrounds, and lighting conditions. To enhance the generalization of person reID models, we propose a new approach consisting of three components: GAN-based data augmentation, cross-domain learning, and evaluation modules. Particularly, Generative Adversarial Network (GAN) approaches are used first to generate synthetic data from real source data by diversifying the environmental condition of the dataset. We then propose a cross-domain learning approach powered by image quality assessment (IQA) to reduce the impact of low-quality images in the combined source data, including synthetic and real source data. The extensive experiments evaluate the superiority of our proposed method over state-of-the-art methods on two famous person reID benchmarks, namely DukeMTMC-reID and Market-1501.