Since the recognition of 2D marginal Fisher analysis is sensitive to the size of neighbors and a sole feature extraction method can’t meet the requirements of further improving recognition rate for variations in expression and pose of facial images, 2D Nonparametric Marginal Fisher Analysis (2DNMFA) based on person correlation coefficient is proposed firstly, and then a novel algorithm is proposed which integrated with 2DNMFA and Singular Value Decomposition (SVD). The algorithm extracts algebraic features and identifiable structural characteristics by SVD and 2DMFA method respectively, and then fuses membership degree by using fuzzy decision theory based on the advantages of two characteristics. Experimental results on CIS face database, Texas face database and UMIST face database demonstrate that this algorithm improves the recognition rate and is more robust than 2DMFA or SVD.