Person Re-Identification (ReID) struggles with discriminative feature learning due to extreme intra-class variance and ambiguous boundary samples. Existing metric losses are often constrained by local mini-batch mining or rigid distance margins that ignore contextual data structures. To address these issues, we propose Se-ReID, a unified framework that enhances feature space representation through instance-level and centroid-level innovations. At the instance level, we introduce TriHard+ Loss with dynamic routing to prevent manifold collapse, alongside an alternative TriWeight Loss utilizing hard-adapted soft weighting to preserve dense intra-class structures. At the centroid level, we propose CentroidM Loss, which leverages learnable global proxies to transcend mini-batch limitations and effectively soften inter-class boundaries. These core metric modules are further supported by 1st & 2nd order mask techniques to eliminate sampling bias, and a streamlined cross-camera centroid retrieval strategy to filter gallery noise. Extensive experiments demonstrate that Se-ReID achieves remarkable performance on standard benchmarks (Market1501 and DukeMTMC-ReID) without relying on ReRank. Notably, it yields state-of-the-art (SOTA) results when integrated with the SOLIDER Transformer baseline, confirming its robust effectiveness and broad applicability across diverse architectures.improvements on MNIST. The code will be released.