Palmprint deformation, including rotation, displacement, swelling and so on, is a crucial factor for the palmprint recognition performance. The traditional algorithms mainly focus on the feature-based matching and the block-based matching. In this paper, a novel block-based matching palmprint recognition scheme is proposed. The sampling points on the template image are built by the harris corner detection algorithm. Furthermore, the corresponding sampling points on the query palmprint image are estimated by image pyramids algorithm for reducing the impact of distortion. At last, a matching method based on statistical offset is proposed to calculate the final matching score. Extensive experiments on public palmprint database shows that our method can achieve excellent recognition performance compared with many other classic methods.