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The abbreviation IWWAM in the Table 2 denotes 'integration weight without ancillary measures'.
We have compared the performance of the proposed method with the baseline techniques such as bimodal systems with equal weighting, optimal integration weight estimation scheme without ancillary measures [21] and integration weight estimation using reliability measures [20, 22].
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These findings were confirmed with ancillary measures.
In this paper, we have considered the utility of multi-normalization and ancillary measures, for the optimal score level fusion of fingerprint and voice biometrics.
To the best of our knowledge, the proposed optimal fusion strategy using multi-normalization, ancillary measures, and optimization techniques has not been attempted until now.
Integration weight optimization techniques based on multi-normalization and ancillary measures are proposed here for improving the performance of the fingerprint and voice biometrics system.
The experimental results show that optimal integration weights estimated using multi-normalization and ancillary measures improves the performance of the multibiometric systems even under low SNR conditions.
Hence, by incorporating ancillary measures using the multi-normalization framework, we could achieve better recognition performance even at low SNR conditions.
Results indicate also that economic instruments such as input and emission taxes could be only ancillary measures to address mitigation in agriculture.
To further enhance the usefulness of this resource, ancillary measures obtained from other sources are also provided for each pair.
When using ancillary measures as benchmarks for patient improvement, the UFS-QOL was able to discriminate among levels of change in patient-reported overall treatment effect and satisfaction.
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