Face　and　facial　expression　recognition　is　a　broad　research　domain　in　machine　learning　domain.　Non-negative　matrix　factorization　(NMF)　is　a　very　recent　technique　for　data　decomposition　and　image　analysis.　Here　we　propose　face　identification　system　as　well　as　a　facial　expression　recognition,　which　is　a　system　based　on　NMF.　We　get　a　significant　result　for　face　recognition.　We　test　on　CK+　and　JAFFE　dataset　and　we　find　the　face　identification　accuracy　is　nearly　99%　and　96.5%　respectively.　But　the　facial　expression　recognition　(FER)　rate　is　not　as　good　as　it　required　for　the　real　life　implementation.　To　increase　the　detection　rate　for　facial　expression　recognition,　our　propose　fusion　based　NMF,　named　as　OEPA-NMF,　where　OEPA　means　Optimal　Expressionspecific　Parts　Accumulation.　Our　experimental　result　shows　OEPA-NMF　outperforms　the　prevalence　NMF　for　facial　expression　recognition.　As　face　identification　using　NMF　has　a　good　accuracy　rate,　so　we　are　not　interested　to　apply　OEPA-NMF　for　face　identification.