<p>Person re-identification (Re-ID) plays a crucial role in the domains of security surveillance and pedestrian behavior analysis, as it aims to retrieve specific individuals captured by different cameras. However, the task of Re-ID remains immensely challenging in the field of computer vision, primarily due to the extensive intra-class variations exhibited by individuals across cameras. These variations include occlusions, illuminations, viewpoints, and poses. In this paper, we present a novel Re-ID framework that addresses the inherent issues related to intra-class variations. Our proposed approach incorporates both auxiliary-domain classification (ADC) and layered semi-second-order information bottleneck (LyrS²IB) techniques. By incorporating ADC as an auxiliary task, we leverage coarse-grained essential features that effectively distinguish individuals from the background. This enables the development of both coarse- and fine-grained feature representations for Re-ID. Furthermore, our framework integrates LyrS²IB to handle redundancy, irrelevance, and noise present in Re-ID features resulting from intra-class variations. This integration allows us to compress and optimize these features without incurring additional computation overhead during inference. Extensive experiments validate the efficacy of our proposed method, demonstrating a significant reduction in the neural network output variance of intra-class person images, firmly establishing the superior performance of our approach in the field of Re-ID.</p>