Motivated by the facts that, (1), the spatial structure of images and the correlation among color channels are important for color face recognition, and (2), natural face images may be occluded, in this work, we propose two-dimensional quaternion sparse principle component analysis (2DQSPCA) to extract features for color face recognition. 2DQSPCA inherently takes the advantage of 2DPCA in preserving the structure of two-dimensional data, as well as the strength of quaternions in representing color images holistically. Benefited from the sparsity constraints, 2DQSPCA is robust for occlusions. Experiments demonstrate the superior performance of 2DQSPCA on color face recognition, especially with occlusions.