Monogenic signal analysis has been increasingly used in face recognition. However

the monogenic orientation has not been fully utilized which as an extremely important geometric information. In this paper

a novel coding method named EPMOD (enhanced patterns of monogenic orientation difference) is proposed to extract the local orientation features. Then a new face recognition method fusing MBP (monogenic binary pattern) and EPMOD is proposed. First

MBP feature and EPMOD feature are extracted by using multi-scale monogenic filter; then

BFLD (block-based Fisher linear discrimination) is used to reduce the dimensionality of the two descriptors. Finally

the two kind of feature is fused at score level. The experimental results on the ORL and CAS-PEAL face databases validate that the proposed algorithm has better performance than or comparable performance than LGBP and MBP but with lower time and space complexity. An effective facial feature extraction method is proposed in this paper

and the experimental results also show that our fusion approach can improve the recognition rate significantly.