Finding the accurate corresponded landmarks from a collection of shape instances plays critical role in constructing active shape models (ASMs). We have developed a global consistent shape correspondence paradigm for efficient and effective active shape models to address challenging issues in statistical shape modelling. Specifically, in this paper, we developed techniques to perform a fast multiple shape matching to identify global consistent shape correspondence from a set of training shape instances via efficient low-rank recovery optimization. High quality ASMs can then be constructed based on the identified corresponded points. The entire process is unsupervised without manual annotation as well as free of selection of anatomically significant point. Experimental results on mobile hand image data demonstrate the superior performance of our proposed method over other state-of-the-art techniques like MDL in constructing active shape models.