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State-of-the-art face recognition algorithms are able to achieve goodperformance when sufficient training images are provided. Unfortunately, thenumber of facial images is limited in some real face recognition applications.In this paper, we propose ComplexFace, a novel and effective algorithm for facerecognition with limited samples using complex number based data augmentation.The algorithm first generates new representations from original samples andthen fuse both into complex numbers, which avoids the difficulty of weightsetting in other fusion approaches. A test sample can then be expressed by thelinear combination of all the training samples, which mapped the sample to thenew representation space for classification by the kernel function. Thecollaborative representation based classifier is then built to makepredictions. Extensive experiments on the Georgia Tech (GT) face database andthe ORL face database show that our algorithm significantly outperformsexisting 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 ofprevious approaches ranging from 5.21% to 10.99% are reduced to 1.67% over theORL database. In other words, our algorithm has decreased the average errors byup to 84.80% on the ORL database.

ComplexFace: a Multi-Representation Approach for Image Classification with Small Dataset