A two-dimensional Neighborhood Preserving Projection for appearance-based face recognition

This paper presents a two-dimensional Neighborhood Preserving Projection (2DNPP) for appearance-based face representation and recognition. 2DNPP enables us to directly use a feature input of 2D image matrices rather than 1D vectors. We use the same neighborhood weighting procedure that is involved in NPP to form the nearest neighbor affinity graph. Theoretical analysis of the connection between 2DNPP and other 2D methods is presented as well. We conduct extensive experimental verifications to evaluate the performance of 2DNPP on three face image datasets, i.e. ORL, UMIST, and AR face datasets. The results corroborate that 2DNPP outperforms the standard NPP approach across all experiments with respect to recognition rate and training time. 2DNPP delivers consistently promising results compared with other competing methods such as 2DLPP, 2DLDA, 2DPCA, ONPP, OLPP, LPP, LDA, and PCA.

► We propose a 2D Neighborhood Preserving Projection (2DNPP) for face representation.
► DNPP uses a feature input of 2D image matrices rather than 1D vectors.
► The relation between 2DNPP and other 2D methods is theoretically analyzed.
► 2DNPP outperforms the standard NPP according to experimental results.
► 2DNPP delivers consistently promising results compared with other methods.