The traditional multi-view dictionary learning algorithm is designed to take advantage of the correlation between multi-view data and fails to consider the distinctiveness of the multi-view data,which may reduce the performance of dictionary.Inspired by this observation,we present a multi-view dictionary learning based on the intra-view atom inconsistency algorithm.The algorithm learns class-specific dictionaries and the shared class dictionary for each view and calculates the minimum of the coding coefficient variance to reduce the distinctiveness of inter-view dictionaries.In addition,the minimization of the weighted sum of the distance between the coding coefficients between each view and the mean of coding coefficients for all views restrict the contribution of the corresponding features.Then,we embed the inconsistency constraint into the intra-view dictionaries to reduce redundancy.Finally,two datasets (AR and Extended Yale B datasets) were used to validate the effectiveness of the proposed algorithm.