research-article

Adaptive feature set updating algorithm for multimodal biometrics

Authors:

H B Kekre,

V A BharadiAuthors Info & Claims

ICAC3 '09: Proceedings of the International Conference on Advances in Computing, Communication and Control

Pages 277 - 282

https://doi.org/10.1145/1523103.1523161

Published: 23 January 2009 Publication History

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Abstract

Multimodal Biometrics is an emerging domain in biometric technology where we combine more than one biometric trait to improve the performance. Here we propose a new concept that can be used in designing future multimodal biometrics systems which can adapt to the change in the biometrics features like face, voice, signature, and gait over the time or any other factor without compromising the security. We adapt to the change in biometric features by updating feature set without re-enrollment of entire set. This technique requires use of at least one biometric feature which has very low variance or high degree of permanence, like Fingerprint, Iris, Retina etc. This algorithm can address the problem of false rejection caused by sustained change in biometric features due to aging or any other factor without the need of re-enrollment of feature set. We suggest architecture of Multimodal biometric system implementing this algorithm.

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Cited By

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Yang YGuo BLiang YYu Z(2021)Detection of Behavior Aging from Keystroke Dynamics2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)10.1109/ICPADS53394.2021.00078(583-590)Online publication date: Dec-2021
https://doi.org/10.1109/ICPADS53394.2021.00078
Pisani PMhenni AGiot RCherrier EPoh NFerreira de Carvalho ARosenberger CAmara N(2019)Adaptive Biometric SystemsACM Computing Surveys10.1145/334425552:5(1-38)Online publication date: 13-Sep-2019
https://dl.acm.org/doi/10.1145/3344255

Index Terms

Adaptive feature set updating algorithm for multimodal biometrics
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
  2. Computer graphics
    1. Image manipulation

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ICAC3 '09: Proceedings of the International Conference on Advances in Computing, Communication and Control

January 2009

707 pages

ISBN:9781605583518

DOI:10.1145/1523103

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 January 2009

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University Grants Commission

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ICAC3 '09

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SIGAI

ICAC3 '09: International Conference on Advances in Computing, Communication and Control

January 23 - 24, 2009

Mumbai, India

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Cited By

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Yang YGuo BLiang YYu Z(2021)Detection of Behavior Aging from Keystroke Dynamics2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)10.1109/ICPADS53394.2021.00078(583-590)Online publication date: Dec-2021
https://doi.org/10.1109/ICPADS53394.2021.00078
Pisani PMhenni AGiot RCherrier EPoh NFerreira de Carvalho ARosenberger CAmara N(2019)Adaptive Biometric SystemsACM Computing Surveys10.1145/334425552:5(1-38)Online publication date: 13-Sep-2019
https://dl.acm.org/doi/10.1145/3344255

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Recommendations

Fusion of face and iris features for multimodal biometrics

Continuous user authentication using multi-modal biometrics

Non-stationary feature fusion of face and palmprint multimodal biometrics