Learn More
Score level fusion is an appealing method for combining multi-algorithms, multirepresentations, and multi-modality biometrics due to its simplicity. Often, scores are assumed to be independent, but even for dependent scores, according to the Neyman-Pearson lemma, the likelihood ratio is the optimal score level fusion if the underlying distributions are(More)
In biometric score level fusion, the scores are often assumed to be independent to simplify the fusion algorithm. In some cases, the “average” performance under this independence assumption is surprisingly successful, even competing with a fusion that incorporates dependence. We present two main contributions in score level fusion: (i)(More)
  • 1