Image perceptual hashing is a notable concept in the field of image processing. Its application ranges from image retrieval, image authentication, image recognition, to content-based image management. In this paper a novel image hashing algorithm based on SURF and KPCA, which extracts speed-up robust feature as the perceptual feature, is proposed. SURF retains the robust properties of SIFT, and it is 3 to 10 times faster than SIFT. Then, the Kernel PCA is used to decompose key points’ descriptors and get compact expressions with well-preserved feature information. To improve the precision of digest matching, a binary image template of input image is generated which contains information of salient region to ensure the key points in it have greater weight during matching. After that, the hashing digest for image retrieval and image recognition is constructed. Experiments indicated that compared to SIFT and PCA based perceptual hashing, the proposed method could increase the precision of recognition, enhance robustness, and effectively reduce process time.