Personal identification based on weighting key point scheme for hand image

Authors:
Dongbing Pu;Shuang Qi;Chunguang Zhou;Yinghua Lu
Affiliations:
College of Computer Science & Technology, Jilin University, Changchun, Jilin Province, China and College of Computer, Northeast Normal University, Changchun, Jilin Province, China;College of Computer, Northeast Normal University, Changchun, Jilin Province, China and Key Laboratory for Applied Statistics of MOE, China;College of Computer Science & Technology, Jilin University, Changchun, Jilin Province, China;College of Computer, Northeast Normal University, Changchun, Jilin Province, China
Venue:
IWCIA'08 Proceedings of the 12th international conference on Combinatorial image analysis
Year:
2008

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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.