With the improvement of the performance of infrared sensors and the popularization of applications, it becomes possible to obtain multi-view images of the same target in the same scene. Therefore, a target recognition method combining multi-view infrared images was proposed. First, the clustering analysis on multi-view infrared images was performed to obtain multiple view-view subsets. In each view subset, the infrared images shared high correlations. For different view subsets, they were relatively independent. In order to make full use of the correlation and independence, the joint sparse representation (JSR) was used to make decisions on single view subsets. In particular, for the subset with only one view, the classical sparse representation-based classification (SRC) was directly used for decision. For the decision results obtained by different view subsets, the fusion processing was carried out based on the idea of linear weighting. And the target category was determined according to the fused results. Therefore, on the basis of analyzing the inner correlation of the multi-view infrared images, the proposed method separately examined the local correlations and overall independence, and integrated them through the fusion on the decision-making layer, which improved the reliability of the final decision. Experiments were performed on the collected infrared images of multiple types of traffic vehicles. The proposed method was tested and verified on the original, noisy, and occluded samples. The effectiveness of the proposed method is verified by comparison with other methods.