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12 March 1999 Multilevel data fusion approach for gradually upgrading the performances of identity verification systems
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The aim of this paper is to propose a strategy that uses data fusion at three different levels to gradually improve the performance of an identity verification system. In a first step temporal data fusion can be used to combine multiple instances of a single (mono-modal) expert to reduce its measurement variance. If system performance after this first step is not good enough to satisfy the end-user's needs, one can improve it by fusing in a second step result of multiple experts working on the same modality. For this approach to work, it is supposed that the respective classification errors of the different experts are de-correlated. Finally, if the verification system's performance after this second step is still not good enough, one will be forced to move onto the third step in which performance can be improved by using multiple experts working on different (biometric) modalities. To be useful however, these experts have to be chosen in such a way that adding the extra modalities increases the separation in the multi-dimensional modality-space between the distributions of the different populations that have to be classified by the system. This kind of level-based strategy allow to gradually tune the performance of an identity verification system to the end-user's requirements while controlling the increase of investment costs. In this paper results of several fusion modules will be shown at each level. All experiments have been performed on the same multi-modal database to be able to compare the gain in performance each time one goes up a level.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Patrick S. A. Verlinde, Pascal Druyts, Gerard Chollet, and Marc P. J. Acheroy "Multilevel data fusion approach for gradually upgrading the performances of identity verification systems", Proc. SPIE 3719, Sensor Fusion: Architectures, Algorithms, and Applications III, (12 March 1999);

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