  State-of-the-art face recognition algorithms are able to achieve good
performance when sufficient training images are provided. Unfortunately, the
number of facial images is limited in some real face recognition applications.
In this paper, we propose ComplexFace, a novel and effective algorithm for face
recognition with limited samples using complex number based data augmentation.
The algorithm first generates new representations from original samples and
then fuse both into complex numbers, which avoids the difficulty of weight
setting in other fusion approaches. A test sample can then be expressed by the
linear combination of all the training samples, which mapped the sample to the
new representation space for classification by the kernel function. The
collaborative representation based classifier is then built to make
predictions. Extensive experiments on the Georgia Tech (GT) face database and
the ORL face database show that our algorithm significantly outperforms
existing methods: the average errors of previous approaches ranging from 31.66%
to 41.75% are reduced to 14.54% over the GT database; the average errors of
previous approaches ranging from 5.21% to 10.99% are reduced to 1.67% over the
ORL database. In other words, our algorithm has decreased the average errors by
up to 84.80% on the ORL database.
