The objective of this paper is to study Interest Points (IP) filtering in video-based human re-identification tasks. The problem is that having a large number of IPs to describe person, Re-identification grows into a much time consuming task and IPs become redundant. In this context, we propose a Two-Stage filtering step. The first stage reduces the number of IP to be matched and the second ignores weak matched IPs participating in the re-identification decision. The proposed approach is based on the supervision of SVM, learned on training dataset. Our approach is evaluated on the dataset PRID-2011 and results show that it is fast and compare favorably with the state of the art