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Local feature points have been widely investigated in solving problems in computer vision, such as robust matching and object detection. However, its investigation in the area of image hashing is still limited. In this paper, we propose a novel shape-contexts-based image hashing approach using robust local feature points. The contributions are twofold: 1) The robust SIFT-Harris detector is proposed to select the most stable SIFT keypoints under various content-preserving distortions. 2) Compact and robust image hashes are generated by embedding the detected local features into shape-contexts-based descriptors. Experimental results show that the proposed image hashing is robust to a wide range of distortions and attacks, due to the benefits of robust salient keypoints detection and the shape-contexts-based feature descriptors. When compared with the current state-of-the-art schemes, the proposed scheme yields better identification performances under geometric attacks such as rotation attacks and brightness changes, and provides comparable performances under classical distortions such as additive noise, blurring, and compression. Also, we demonstrate that the proposed approach could be applied for image tampering detection.