Personalized learning and decision for multimodal biometrics

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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 languageEnglish
Title of host publication2004 IEEE Conference on Cybernetics and Intelligent Systems
Pages1111-1116
Number of pages6
Publication statusPublished - 2004 Dec 1
Event2004 IEEE Conference on Cybernetics and Intelligent Systems - , Singapore
Duration: 2004 Dec 12004 Dec 3

Publication series

Name2004 IEEE Conference on Cybernetics and Intelligent Systems

Other

Other2004 IEEE Conference on Cybernetics and Intelligent Systems
CountrySingapore
Period04/12/104/12/3

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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  • Cite this

    Toh, K. A. (2004). Personalized learning and decision for multimodal biometrics. In 2004 IEEE Conference on Cybernetics and Intelligent Systems (pp. 1111-1116). (2004 IEEE Conference on Cybernetics and Intelligent Systems).