This paper proposes a robust recognition method based on dual attention calibration to address the issues of insufficient multi-dimensional dynamic collaboration and fine-grained suppression in the attention mechanism under mask occlusion. The method dynamically calibrates the occlusion area in both channel and spatial dimensions. The channel dimension is based on global statistics to suppress abnormal responses of polluted channels, while the spatial dimension locates occluded areas and weakens their gradient propagation, achieving dynamic calibration from coarse-grained screening to fine-grained enhancement. On this basis, the weighted cross entropy loss and triplet loss are used to further guide the model to focus on the feature expression of locally unobstructed areas, thereby expanding the inter class feature distance interval. The experimental results show that the dual attention calibration mechanism proposed in this paper, through the synergistic effect of channel dimension feature screening and spatial dimension region enhancement, has improved accuracy by 6% and 7.2% respectively compared to the ArcFace algorithm in mask scenes of LFW and AgeDB-30, and by 7.3% on the real occlusion dataset MAFA dataset, verifying its recognition robustness in complex occlusion scenes.