Human identification using KnuckleCodes

Ajay Kumar Pathak; Yingbo Zhou

doi:10.1109/BTAS.2009.5339021

Human identification using KnuckleCodes

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

82 Citations (Scopus)

Abstract

The usage of finger knuckle images for personal identification has shown promising results and generated lot of interest in biometrics. In this work, we investigate a new approach for efficient and effective personal identification using KnuckleCodes. The enhanced knuckle images are employed to generate KnuckleCodes using localized Radon transform that can efficiently characterize random curved lines and creases. The similarity between two KnuckleCodes is computed from the minimum matching distance that can account for the variations resulting from translation and positioning of fingers. The feasibility of the proposed approach is investigated on the finger knuckle database from 158 subjects. The experimental results, i.e., equal error rate of 1.08% and rank one recognition rate of 98.6%, suggest the utility of the proposed approach for online human identification.

Original language	English
Title of host publication	IEEE 3rd International Conference on Biometrics
Subtitle of host publication	Theory, Applications and Systems, BTAS 2009
DOIs	https://doi.org/10.1109/BTAS.2009.5339021
Publication status	Published - 16 Dec 2009
Event	IEEE 3rd International Conference on Biometrics: Theory, Applications and Systems, BTAS 2009 - Washington, DC, United States Duration: 28 Sept 2009 → 30 Sept 2009

Conference

Conference	IEEE 3rd International Conference on Biometrics: Theory, Applications and Systems, BTAS 2009
Country/Territory	United States
City	Washington, DC
Period	28/09/09 → 30/09/09

ASJC Scopus subject areas

Biotechnology
Computational Theory and Mathematics
Computer Vision and Pattern Recognition

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10.1109/BTAS.2009.5339021

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title = "Human identification using KnuckleCodes",

abstract = "The usage of finger knuckle images for personal identification has shown promising results and generated lot of interest in biometrics. In this work, we investigate a new approach for efficient and effective personal identification using KnuckleCodes. The enhanced knuckle images are employed to generate KnuckleCodes using localized Radon transform that can efficiently characterize random curved lines and creases. The similarity between two KnuckleCodes is computed from the minimum matching distance that can account for the variations resulting from translation and positioning of fingers. The feasibility of the proposed approach is investigated on the finger knuckle database from 158 subjects. The experimental results, i.e., equal error rate of 1.08% and rank one recognition rate of 98.6%, suggest the utility of the proposed approach for online human identification.",

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AB - The usage of finger knuckle images for personal identification has shown promising results and generated lot of interest in biometrics. In this work, we investigate a new approach for efficient and effective personal identification using KnuckleCodes. The enhanced knuckle images are employed to generate KnuckleCodes using localized Radon transform that can efficiently characterize random curved lines and creases. The similarity between two KnuckleCodes is computed from the minimum matching distance that can account for the variations resulting from translation and positioning of fingers. The feasibility of the proposed approach is investigated on the finger knuckle database from 158 subjects. The experimental results, i.e., equal error rate of 1.08% and rank one recognition rate of 98.6%, suggest the utility of the proposed approach for online human identification.

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M3 - Conference article published in proceeding or book

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T2 - IEEE 3rd International Conference on Biometrics: Theory, Applications and Systems, BTAS 2009

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ER -

Human identification using KnuckleCodes

Abstract

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ASJC Scopus subject areas

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