  Collaborative Representation Classification (CRC) for face recognition
attracts a lot attention recently due to its good recognition performance and
fast speed. Compared to Sparse Representation Classification (SRC), CRC
achieves a comparable recognition performance with 10-1000 times faster speed.
In this paper, we propose to ensemble several CRC models to promote the
recognition rate, where each CRC model uses different and divergent randomly
generated biologically-inspired features as the face representation. The
proposed ensemble algorithm calculates an ensemble weight for each CRC model
that guided by the underlying classification rule of CRC. The obtained weights
reflect the confidences of those CRC models where the more confident CRC models
have larger weights. The proposed weighted ensemble method proves to be very
effective and improves the performance of each CRC model significantly.
Extensive experiments are conducted to show the superior performance of the
proposed method.
