Being a new biometrical characteristic, knuckleprint has drawn considerable attention in personal identification. But owing to its defects such as weak resistance to noise, easy affection by hand-shape, etc., the location of region of interest (ROI) of knuckleprint is more difficult than other biometrical characteristics of palm. In order to solve the problem of accurate location of ROI of knuckleprint, by defining ROI of knuckleprint as the minimal region containing the total information of knuckleprint, a new automatic detection and location algorithm is brought forward based on wavelet multi-resolution and Radon projection to locate the ROI of knuckleprint accurately. Based on the texture similar theory, this algorithm divides knuckleprint image into multi-dimension images which include several high frequency sub-images and low frequency sub-images by wavelet multi-resolution, and then uses feature vector and regional growth to produce candidate sub-region set in high frequency sub-images. After that, the algorithm utilizes Radon projection in low frequency sub-images to verify the candidate sub-region set obtained from high frequency sub-images. Finally, by adopting straight-line fitting technique, the location of ROI of knuckleprint in original image is accurately located. Emulation experiment shows that this algorithm not only can get rid of noise and hand-shape affection but also can keep robust in different situations.