In this paper, we study compression and identifica- tion algorithms for the identification systems using polar codes. High dimensional feature vectors representing users are first compressed and then enrolled in a database. When an unknown enrolled user is observed, the noisy observation is compared with the entries in the database and the processing unit outputs an estimated user index. We develop three approaches based on polar codes and apply them to identification systems. This is the first time that identification system based on polar codes is studied. In particular, the identification mapping is challenging. The proposed methods provide a framework of applying polar codes to identification systems. The numerical evaluation results show that they results in complexity linearly depends on the number of users and low identification error rates as the sequence length increases.