Abstract: The extraction of image local features is a key problem in the field of image understanding and computer vision. SIFT has been widely used owing to its distinctiveness and robustness. However, SIFT is not robust to affine deformations, because it is based on DOG detector which extracts circle regions for keypoint location. Besides, SIFT descriptor is represented by a 128 element feature vector , and when it is used for image registration especially for the case that there are many keypoints in the image , the matching speed will be slow and storage requirement will be huge. In order to overcome these disadvantages, a novel descriptor named HA-DR-SIFT (Hessian Affine-Dimensionality Reduction-SIFT) that is robust to affine deformations is proposed. Firstly, Hessian-Affine Detector instead of DOG detector for keypoint location detection is used to make the elliptical region affine invariant. Then, the PCA or NLPCA method for new descriptor's dimension reduction is used to improve successive computing efficiency. The experiments show that the novel descriptor is not only invariant to affine changes, but also has higher matching speed and lower storage requirement than SIFT.