(untitled) (International Journal of Artificial Intelligence Engineering and Systems, 2026)
Person re-identification across diverse environmental conditions remains a formidable challenge in intelligent surveillance and large-scale visual search systems. Domain shift caused by variations in illumination, weather, camera hardware, and scene geometry undermines the reliability of learned feature representations, demanding new strategies that go beyond conventional domain adaptation. This paper presents an adaptive domain generalization framework that harnesses feature decorrelation networks to learn domain-invariant yet discriminative person representations suitable for cross-environment deployment. At the architectural core, dedicated decorrelation modules enforce statistical independence among feature dimensions by systematically suppressing spurious correlations that capture environment-specific patterns, while a meta-adaptive controller dynamically tunes the decorrelation strength in response to the estimated distributional characteristics of the target domain without requiring target labels. The system-level discussion extends to the interplay between decorrelation depth, computational overhead, and recognition accuracy, as well as the integration of the proposed networks into edge–cloud infrastructures, addressing real-time constraints, model compression, and energy footprint. Furthermore, the paper examines the fairness implications of decorrelation across demographic groups, highlighting how rigorous feature orthogonality can both mitigate and inadvertently amplify biases depending on the tuning of invariance constraints. Through this multifaceted lens, the study offers a holistic evaluation of feature decorrelation networks as a sustainable, ethical, and robust paradigm for adaptive person re-identification in unconstrained environments, charting pathways for future research at the intersection of machine learning, systems engineering, and societal governance.