Similarity measure has long played a critical role and attracted great interest in various areas such as patte recognition and machine perception. Nevertheless, there remains the issue of developing an efficient two-dimensional (2D) robust similarity measure method for images. Inspired by the properties of subspace, we develop an effective 2D image similarity measure tech-nique, named transformation similarity measure (TSM), for robust face recognition. Specifically, the TSM method robustly de-termines the similarity between two well-aligned frontal facial images while weakening interference in the face recognition by linear transformation and singular value decomposition. We present the mathematical features and some odds to reveal the feasible and robust measure mechanism of TSM. The performance of the TSM method, combined with the nearest neighbor rule, is evaluated in face recognition under different challenges. Experimental results clearly show the advantages of the TSM method in terms of accuracy and robustness.