Multimodal Personal Verification Using Likelihood Ratio for the Match Score Fusion
Joint Authors
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-10-31
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
In this paper, the authors present a novel personal verification system based on the likelihood ratio test for fusion of match scores from multiple biometric matchers (face, fingerprint, hand shape, and palm print).
In the proposed system, multimodal features are extracted by Zernike Moment (ZM).
After matching, the match scores from multiple biometric matchers are fused based on the likelihood ratio test.
A finite Gaussian mixture model (GMM) is used for estimating the genuine and impostor densities of match scores for personal verification.
Our approach is also compared to some different famous approaches such as the support vector machine and the sum rule with min-max.
The experimental results have confirmed that the proposed system can achieve excellent identification performance for its higher level in accuracy than different famous approaches and thus can be utilized for more application related to person verification.
American Psychological Association (APA)
Binh Tran, Long& Le, Thai Hoang. 2017. Multimodal Personal Verification Using Likelihood Ratio for the Match Score Fusion. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1141288
Modern Language Association (MLA)
Binh Tran, Long& Le, Thai Hoang. Multimodal Personal Verification Using Likelihood Ratio for the Match Score Fusion. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1141288
American Medical Association (AMA)
Binh Tran, Long& Le, Thai Hoang. Multimodal Personal Verification Using Likelihood Ratio for the Match Score Fusion. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1141288
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1141288