DocumentCode :
3481261
Title :
Personalized learning and decision for multimodal biometrics
Author :
Kar-Ann Toh
Author_Institution :
Inst. for Infocomm Res., Singapore
Volume :
2
fYear :
2004
fDate :
1-3 Dec. 2004
Firstpage :
1112
Lastpage :
1117
Abstract :
In this paper, we address the multi-modal biometric decision fusion problem. By exploring into the user-specific approach for learning and threshold setting, four possible paradigms for learning and decision making are investigated. Since each user requires a decision hyperplane specific to him in order to achieve good verification accuracy, those tedious iterative training methods like the neural network approach would not be suitable. We propose to use a model which requires only a single training step for this application. The four global and local learning and decision paradigms are then explored to observe their decision capabilities. Besides proposal of a relevant receiver operating characteristic performance for local decision, extensive experiments were conducted to observe the verification performance for fusion of three biometrics
Keywords :
biometrics (access control); decision making; learning (artificial intelligence); pattern classification; decision hyperplane; decision making; multimodal biometric decision fusion problem; multivariate polynomials; pattern classification; pattern recognition; personalized decision; personalized learning; Biometrics; Decision making; Iterative methods; Neural networks; Pattern classification; Pattern recognition; Proposals; Support vector machine classification; Support vector machines; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cybernetics and Intelligent Systems, 2004 IEEE Conference on
Conference_Location :
Singapore
Print_ISBN :
0-7803-8643-4
Type :
conf
DOI :
10.1109/ICCIS.2004.1460745
Filename :
1460745
Link To Document :
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