In this paper, a sub-image method based on feature sampling and feature fusion (called as RS_SpCCA) is proposed. RS_SpCCA first performs a random subspace method in sub-images which are partitioned in a deterministic way. Then, the method obtains correlation features by fusing sampled features and global feature extracted by certain feature extraction method and finally, constructs component classifiers on corrleation features. In this method, the purpose of sampling feature is to construct more diverse component classifiers, and the purpose of the fusing feature is to make good use of the global information. The experimental results on AR, Yale and ORL three face image databases show that sub-image method based on feature sampling and feature fusion (RS_SpCCA) is superior to both SpCCA and Semi-RS which only use feature sampling or feature fusion.