Scale, affine variance and complex background are important factors affecting the accuracy of license plate location. We presented a method of license plate location based on multi-scale product of corner detection and visual color features in difference of Gaussian (DOG) scale space. Based on the image edge information in DOG scale space, we first extracted scale-and-affine-invariant corner and color features through multi-scale multiplication, and then obtained the candidate license plate location by fusing the corner and the color features. Finally, we accurately located the license plate by using the distance between the feature points in the plate region and the intensive relationship of the points. Experiments on several real-world vehicle image data sets under complex conditions have verified the proposed method has high effectiveness and efficiency in locating license plates, and greater performance in robustness of noise and affine variance than other state-of-art license plate localization methods.