Abstract: Modality entanglement introduced by spectral differences and heterogeneity interference reduces the accuracy of cross-modality person re-identification. To suppress the degradation of discrimination characteristics and heterogeneity interference caused by modality entanglement, a Plaque-Decoupling Self-Collaborative Alignment model is proposed, which consists of a plaque-decoupling and self-collaborative alignment modules. To mine the intra-class feature discrimination information, the plaque-decoupling module decomposes the cross-modality features in the channel dimension and extracts patch features, which can suppress the imaging modality entanglement of the visible and infrared images. The self-collaborative alignment module includes diversity decoupling and alignment constraints that can filter abnormal features caused by lighting factors and eliminate inter-class differences. The diversity-decoupling constraints learn the diversity-decoupling features, and the decoupled information is used by the alignment constraints to match the features. Experimental results on the SYSU-MM01 dataset demonstrate the effectiveness of the method.