Sentence examples for verification scenario from inspiring English sources

We present a methodology of reliability estimation in the multimodal biometric verification scenario.

In the verification scenario, we see that the quality-independent approaches such as Sum, GMM, LLR, and RankSVM do not perform well, with EERs of more than 25%.

We provide adaptive algorithms for both the verification scenario (given an independent set, is it X?) and the discovery scenario (find X without any information).

With regard to the identification scenarios (see Fig. 11 and Table 10), the accuracy trend for each benchmark is, however, slightly different from that for the verification scenario, i.e., the RankSVM achieves the best or second best results for all qualities except for the rank-1 identifix cation rate of Q L L85 for multi-modal fusion.

Since Nandakumar et al. [65] proposed a method to extend the fusion for the verification scenario (i.e., using a likelihood ratio-based approach) into that for the identification scenario, it may be expected that the best benchmark for the verification will also achieve the highest accuracy in the identification scenario.

The simplified model is verified against the full-order model for some verification scenarios.

Both identification and verification scenarios are considered, and appropriate performance statistics reported for each case.

We presented the implementation results with various verification scenarios using Omnia terminal, LED TV, and Note PC.

An example project is used to present different verification scenarios along with strategies of how to solve each problem.

Furthermore, in order to check complex behavior of verified systems, different verification scenarios must be prepared and this is the role of random generators or effective regression tests scenarios.

Moreover, we see that some of the probability density-based methods such as GMM and LLR and classification-based approach such as RankSVM perform stably and well in verification scenarios (see Fig. 10) because probability density-based approaches guarantee optimality in terms of the ROC [64] on the condition that the estimated probability densities are correct.