Personal identification based on weighting key point scheme for hand image

Abstract

Biometrics-based personal identification is regarded as an effective method for automatically recognizing a person's identity with a high confidence. This paper presents a novel approach for personal identification using weighting relative distance of key point scheme on hand images. In contrast with the existing approaches, this system extracts multimodal features, including hand shape and palmprint to facilitate the task of coarse-to-fine dynamic identification. Five hand geometrical features are used to guide the selection of a small set of similar candidate samples at the coarse level matching stage. In the fine level matching stage, the weighting relative distance of key point approach is proposed to extract palmprint texture. © 2008 Springer-Verlag Berlin Heidelberg.