<![CDATA[The problem of matching facial features is an important challenge in biometric systems, especially due to variations in lighting, texture and facial details that affect the stability of keypoint detection. This research aims to compare the performance of the Scale-Invariant Feature Transform (SIFT) and Accelerated-KAZE (AKAZE) algorithms in the facial feature extraction and matching process to determine the trade-off between accuracy and computational efficiency. The dataset used comes from NIST with 393 training images and 341 validation images. Evaluation is carried out using the number of detected keypoints, number of matching keypoints, number of inliers and outliers, feature extraction time, as well as error metrics such as MSE, MAE, RMSE, and R². Experimental results show that SIFT produces better matching performance with a total of 934,763 keypoints detected, an average matching keypoint of 121.14, and the number of inliers of 116.95. In addition, SIFT produces lower MSE, MAE, and RMSE values than AKAZE, indicating better feature matching consistency in facial images. However, AKAZE has higher computational efficiency with an average feature extraction time of 0.1699 seconds, faster than SIFT of 0.2928 seconds. The contribution of this research lies in the comparative analysis of the performance of SIFT and AKAZE in keypoint-based facial feature matching, so that it can be a reference in selecting algorithms according to application needs, both oriented towards accuracy and computational efficiency.]]>
