A new finger feature fusion method based on local gabor binary pattern

Abstract

This paper proposes a novel multimodal feature fusion method based on local Gabor binary pattern (LGBP). First, the feature maps of three modalities of finger, fingerprint (FP), finger vein (FV) and finger knuckle print (FKP), are respectively extracted using LGBP. The obtained LGBP-coded maps are further explored using local-invariant gray description to generate Local Gabor based Invariant Gray Features (LGIGFs). To reduce pose variations of fingers in imaging, LGIGFs are then weighed by a Gaussian modal. The experimental results show that the proposed method is capable of fusing multimodal feature effectively, and improve correct recognition rate greatly.