Incremental learning of face recognition based on block non-negative matrix factorization
Xu Chen · Jisuanji yingyong yanjiu · 2009
Non-negative matrix factorization(NMF) can extract local features of images.However,NMF method has two main drawbacks.One shortcoming is that it is very time-consuming to deal with large matrices.The other is that it must implement repetitive learning,when the training samples or classes are incremental.In order to overcome these two limitations,this paper presented a novel block NMF(BNMF) method.In particular,it could be applied to incremental learning.Two face databases,namely FERET and CMU PIE face databases,were selected for evaluation.Comparing with NMF and PCA schemes,the proposed method gives superior results.