In many computer vision tasks, in order to improve the accuracy and robustness to the noise, wavelet analysis is preferred for the natural multi-resolution property. However, the wavelet representation suffers from the dependency of the starting point of the sampled contour. For overcoming the problem that the wavelet representation depends on the starting point of the sampled contour, the Zernike moments are introduced, and a novel Starting-Point-Independent wavelet coefficient shape matching algorithm is presented. The proposed matching algorithm firstly gains the object contours, and give the translation and scale invariant object shape representation. The object shape representation is converted to the dyadic wavelet representation by the wavelet transform. And then calculate the Zernike moments of wavelet representation in different scales. With respect to property of rotation invariant of Zernike moments, consider the Zernike moments as the feature vector to calculate the dissimilarity between the object and template image, which overcoming the problem of dependency of starting point. The experimental results have proved the proposed algorithm to be efficient, precise, and robust.