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.
| Original language | English |
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| Title of host publication | 2004 IEEE Conference on Cybernetics and Intelligent Systems |
| Pages | 1111-1116 |
| Number of pages | 6 |
| Publication status | Published - 2004 Dec 1 |
| Event | 2004 IEEE Conference on Cybernetics and Intelligent Systems - , Singapore Duration: 2004 Dec 1 → 2004 Dec 3 |
Publication series
| Name | 2004 IEEE Conference on Cybernetics and Intelligent Systems |
|---|
Other
| Other | 2004 IEEE Conference on Cybernetics and Intelligent Systems |
|---|---|
| Country | Singapore |
| Period | 04/12/1 → 04/12/3 |
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All Science Journal Classification (ASJC) codes
- Engineering(all)
Cite this
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Personalized learning and decision for multimodal biometrics. / Toh, Kar Ann.
2004 IEEE Conference on Cybernetics and Intelligent Systems. 2004. p. 1111-1116 (2004 IEEE Conference on Cybernetics and Intelligent Systems).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Personalized learning and decision for multimodal biometrics
AU - Toh, Kar Ann
PY - 2004/12/1
Y1 - 2004/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=11244268559&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=11244268559&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:11244268559
SN - 0780386442
SN - 9780780386440
T3 - 2004 IEEE Conference on Cybernetics and Intelligent Systems
SP - 1111
EP - 1116
BT - 2004 IEEE Conference on Cybernetics and Intelligent Systems
ER -