A palmprint recognition method is presented based on sparse representation, which takes advantage of two-dimensional principal component analysis (2D-PCA) of its better data compression property and faster feature extraction speed to generate the palmprint feature image. The 2D-PCA method not only overcomes the shortage of complex calculation by PCA method due to its higher data dimension, but also retains the data structure of original image to obtain better feature. In order to facilitate sparse representation, we take the PCA method to extract features of palmprint feature image to obtain the training samples. In this case, the training samples still retain the data structure of original image and improve the recognition rate compared to simple PCA method. We take the training samples to construct a redundant dictionary, and express the testing samples as a linear combination of dictionary atoms by sparse representation theory. Then, the classification is achieved according to the sparsity of representation coefficient and sparse concentration. Due to the sparsity of representation coefficient, this method reduces the time and space complexity. Andthe experiments show that the recognition rate of this method is obviously higher than traditional method for the Hong Kong Polytechnic University MSpalmprints Database.