Dual-Branch Person Re-Identification Method Based on Feature Consistency

Person re-identification (Re-ID) is vital for intelligent surveillance. Although many existing methods incorporate multi-scale modules to enhance feature discriminability, they often overlook inter-group feature consistency under cross-camera scale and view variations, which can lead to embedding drift and unstable retrieval. To address this issue, we propose a dual-branch Re-ID framework based on feature consistency. First, we introduce a vertical feature-map segmentation strategy that decouples high-level global features into complementary upper- and lower-region representations in a single forward pass. These regional features are then processed by independent bottlenecks and classifiers, improving local semantic discriminability while maintaining global contextual cues. Second, we propose a Geometric-Distribution Alignment Loss (GDALoss) to explicitly enhance robustness to scale and horizontal-flip variations by minimizing the geometric and distributional discrepancies between differently transformed samples of the same identity in the embedding space. Extensive experiments on three benchmarks demonstrate consistent improvements over the baseline. On Market-1501, our method increases mAP by 1.4% and Rank-1 by 1.5%. On DukeMTMC-ReID, it improves mAP by 1.1% and Rank-1 by 2.9%. On MSMT17, it raises mAP by 3.1% and Rank-1 by 5.3%, validating the effectiveness and robustness of the proposed approach.