In this paper, we present a group sparsity constrained patch based label propagation method for multi-atlas automatic brain labeling. The proposed method formulates the label propagation process as a graph-based theoretical framework, where each voxel in the input image is linked to each candidate voxel in each atlas image by an edge in the graph. The weight of the edge is estimated based on a sparse representation framework to identify a limited number of candidate voxles whose local image patches can best represent the local image patch of each voxel in the input image. The group sparsity constraint to capture the dependency among candidate voxels with the same anatomical label is also enforced. It is shown that based on the edge weight estimated by the proposed method, the anatomical label for each voxel in the input image can be estimated more accurately by the label propagation process. Moreover, we extend our group sparsity constrained patch based label propagation framework to the reproducing kernel Hilbert space (RKHS) to capture the nonlinear similarity of patches among different voxels and construct the sparse representation in high dimensional feature space. The proposed method was evaluated on the NA0-NIREP database for automatic human brain anatomical labeling. It was also compared with several state-of-the-art multi-atlas based brain labeling algorithms. Experimental results demonstrate that our method consistently achieves the highest segmentation accuracy among all methods used for comparison.