Abstract: The low light, strong light disturbance, high dust and other environmental conditions underground in coal mines, as well as the similarity of clothing and coal falling on the face of underground personnel, make it difficult to re identify underground personnel with weak features. The existing personnel re identification methods only extract global features and do not fully consider local features, resulting in low accuracy of underground personnel re identification. In order to solve the above problems, a local feature guided label smoothing and optimization method for re-identification of underground personnel with weak features is proposed. This method first extracts global and local features of underground personnel images through convolutional neural networks. Secondly, the k-nearest neighbor similarity is used to calculate the complementarity score between global and local features, in order to measure the degree of similarity between global and local features. Finally, based on the score of feature complementarity, label smoothing is performed on local features and label optimization is performed on global features. The weight of each local feature is dynamically adjusted to improve the label of each local feature. The prediction results of local features are summarized. The more reliable information is used to improve the label as a global feature label, thereby reducing image noise and enhancing feature identification capability. The experimental results show that the method outperforms mainstream personnel re identification methods in terms of mean average precision (mAP), rank-1 accuracy (Rank-1), and mean inverse negative penalty (mINP) on both publicly available datasets and self built datasets containing images of underground personnel. It has good generalization and robustness, and can effectively achieve underground weak feature personnel re identification.