According to complex environment changes of human face images, this paper proposes a robust PCA human face recognition algorithm based on regularized collaborative representation. This algorithm firstly uses collaborative representation to calculate the residuals between the reconstructed samples and test samples, selects the training samples nearing test samples based on the residuals to constitute a new dictionary, then uses robust PCA to conduct low rank and error decomposition in turn on this dictionary, calculates the smoothness and sparseness of the error matrix, and finally builds distinguishing criterion to conduct classified recognition of human face images by the residuals of collaborative representation and the smoothness and sparseness in low rank decomposition. The experiments on the databases such as ORL, AR and so on show that the robust PCA human face recognition algorithm based on regularized collaborative presentation can not only achieve good recognition performance under complex environment change but also maintain the advantages of collaborative presentation and largely reduce the computation time.