Single sample face recognition based on sample augments and MSD fusion
Yan Lin Xu · 2016 IEEE Information Technology, Networking, Electronic and Automation Control Conference · 2016
To overcome the limitation of traditional face recognition methods for single sample face recognition, a single sample face recognition algorithm based on sample augments and MSD fusion is proposed in this paper. First, according to the facial symmetry theory, some relevant information of possible change could be extract to adapt to the future samples. A combination of the original training sample and its virtual average face, symmetrical face as the training sample set is proposed. Then, the maximum scatter difference algorithm was performed on the new training sample set to get the optimal projection matrix, therefore, the features of the training sample and testing facial images could be obtained by projecting them on the optima projection matrix achieved above. During the recognition stage, the fuzzy decision was used to do the classification. Extensive experiment results on famous ORL and FERET show that the algorithm can improve the recognition rate, and has certain robustness.