For the problem of the high computational complexity of the superposed sparse representation based classification (SSRC), the advantages of the collaborative representation based classification (CRC) that has significantly less complexity than SRC is used and its recognition rate is similar to SRC. The face recognition based on superposed collaborative representation based classification (SCRC) is proposed. Based on a prototype plus variation model, the dictionary is assembled by the class centroids and the sample-to-centroid differences in the SCRC, which can leads to a substantial improvement on CRC. The experimental results show that, if the proposed prototype plus variation representation model is applied, the collaborative representation plays a crucial role in face recognition, and performs well even when the dictionary bases are collected under uncontrolled conditions and only a single sample per classes is available. Compared with the other algorithms, SCRC greatly reduces the computational complexity and ensures the recognition rates．