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Likelihood Ratio-Based Biometric Score Fusion
February 2008 (vol. 30 no. 2)
pp. 342-347
ASCII Text
x 
 
Karthik Nandakumar, Yi Chen, Sarat C. Dass, Anil Jain, "Likelihood Ratio-Based Biometric Score Fusion," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 2, pp. 342-347, February, 2008.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2007.70796,
author = {Karthik Nandakumar and Yi Chen and Sarat C. Dass and Anil Jain},
title = {Likelihood Ratio-Based Biometric Score Fusion},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {30},
number = {2},
issn = {0162-8828},
year = {2008},
pages = {342-347},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2007.70796},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Likelihood Ratio-Based Biometric Score Fusion
IS - 2
SN - 0162-8828
SP342
EP347
EPD - 342-347
A1 - Karthik Nandakumar,
A1 - Yi Chen,
A1 - Sarat C. Dass,
A1 - Anil Jain,
PY - 2008
KW - Multibiometric systems
KW - score level fusion
KW - Neyman-Pearson theorem
KW - likelihood ratio test
KW - Gaussian mixture model
KW - image quality
VL - 30
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Multibiometric systems fuse information from different sources to compensate for the limitations in performance of individual matchers. We propose a framework for optimal combination of match scores that is based on the likelihood ratio test. The distributions of genuine and impostor match scores are modeled as finite Gaussian mixture model. The proposed fusion approach is general in its ability to handle (i) discrete values in biometric match score distributions, (ii) arbitrary scales and distributions of match scores, (iii) correlation between the scores of multiple matchers and (iv) sample quality of multiple biometric sources. Experiments on three multibiometric databases indicate that the proposed fusion framework achieves consistently high performance compared to commonly used score fusion techniques based on score transformation and classification.

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Index Terms:
Multibiometric systems, score level fusion, Neyman-Pearson theorem, likelihood ratio test, Gaussian mixture model, image quality
Citation:
Karthik Nandakumar, Yi Chen, Sarat C. Dass, Anil Jain, "Likelihood Ratio-Based Biometric Score Fusion," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 2, pp. 342-347, Feb. 2008, doi:10.1109/TPAMI.2007.70796
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