<p>Person re-identification (Re-ID) remains challenging due to pose variations and scale changes across non-overlapping camera views. In this work, we propose a Multi-state Perception Consistency Constraints Network (MPCC-Net) that extracts discriminative and robust features for person Re-ID. MPCC-Net consists of three primary components. First, a multi-state fused backbone network processes multi-scale and multi-view information. Second, perception consistency constraints enhance feature stability. Third, partition attention modules focus on different body parts to improve local discrimination. Comprehensive experiments on benchmark datasets demonstrate MPCC-Net’s competitive performance, effectively addressing pose and scale variations for accurate person Re-ID. Our source code will also be publicly available at: <a href="https://github.com/sesamecandy/MPCC-Net">https://github.com/sesamecandy/MPCC-Net</a></p>