Abstract
In this paper we investigate the construction of combination functions in identification systems. In contrast to verification systems, the optimal combination functions for identification systems are not known. In this paper we represent the combination function by means of a neural network and explore different methods of its training, so that the identification system performance is optimized. The modifications are based on the principle of utilizing best impostors from each training identification trial. The experiments are performed on score sets of biometric matchers and handwritten word recognizers. The proposed combination methods are able to outperform the likelihood ratio, which is optimal combination method for verification system, as well as, weighted sum combination method optimized for best performance in identification systems.
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