UIN Walisongo's student identity card (KTM) does not have much function other than just for student identification. Even if the function is increased, it can be used for absenteeism at lectures, borrowing books, or double as an automated teller card (ATM). Doing absences using KTM requires a feature matching method for matching the intended KTM image with the KTM that is searched for in the student database. The feature matching process is based on feature detection in images using various methods such as ORB and Scale Invariant Feature Transform (SIFT). We can perform the feature matching method using the Brute-Force method and the Fast Library Approximated Nearest Neighbor (FLANN) on Google Colab with Python. The results of feature matching on the FLANN method are more than the Brute-Force method. The validation of the two image feature matching was carried out using the Root Mean Square Error (RMSE) method, resulting in an average value of 10.424. The purpose of this article is to detect student ID cards with matching features in the image. The FLANN and Brute-Force feature matching methods can be used to detect KTM UIN in images. Keywords: feature matching, SIFT, FLANN, Brute-Force, RMSE