This paper proposes a dog noseprint identification system based on Gabor filter and feature matching. Given images of dog noseprints, the system determines the region of interest, pre-processes the images using adaptive thresholding, extracts features, and performs feature matching to identify dogs. To extract features, we first apply the Gabor filter with 60 directions to images. Then we employ the scale invariant feature transform (SIFT) feature extractor to obtain keypoints that are invariant to image rotation and scaling. The extracted keypoints are compared with reference key-points of a dog noseprint that needs to be identified. To improve the matching accuracy, we present several matching algorithms. Experiments show that the SIFT based identification system method surpasses other methods in terms of accuracy, while the ORB based on system outperforms other methods in terms of speed.