Robust Face Recognition Based on Fusion of 2DPCA with Generic Learning Framework
Liu Fengjua · Video Engineering · 2014
The recognition performance of existing algorithms is seriously impacted by variation of illustration,expression,pose and mask,for which a face recognition algorithm based on 2DPCA improved by generic learning framework is proposed. Firstly,training samples are composited with additional generic learning training samples to increase the number of training samples. Then,classical 2DPCA is used to extract features. Finally,nearest neighbor classifier is used to classify and finish the face recognition work. The effectiveness and robustness of proposed algorithm is verified by experiments on the two baseline face database ORL,FERET and robust face databases AR and extended YaleB. Experimental results show that proposed method has higher recognition accuracy and less time taken comparing with several advanced algorithms,which indicates that it is expected to be applied into robust realtime face recognition system.