Exact(2)
A constant value of γ=0.5 is assigned as an integration weight at all SNR conditions.
Using the explicit representation (7), the strain energy becomes (8) U = ∫ V U d (F AN, t ) dV = ∑ A W A U A d (F A, t ) with strain energy density function U d. W A is an integration weight denoting the fictive volume around integration point A, i.e. (9) W A = ∫ V M A dV Therein, the numerical integration points are chosen to be identical to the support points A of the gradient interpolation.
Similar(58)
In our earlier work, we presented an optimal integration weight estimation scheme for fingerprint and voice biometric under various noise conditions, without using ancillary information [21].
In this paper, we propose an efficient integration weight optimization strategy incorporating both the reliability measures from the score space (dispersion measure) and the separability measures from the feature space (inter-/intra-class distance) and score space (d-prime statistic).
Hence, a modified integration weight β given by Equation 24 is employed to obtain better performance under low SNR conditions [20].
We also presented a reliability-based optimal integration weight estimation scheme for the fingerprint and voice modalities in [22].
For improving the recognition performance of the multibiometric system, we have presented a multi-normalization-based integration weight estimation scheme using separability measures in [22, 37].
We have applied the GS, GA and PSO techniques for optimizing the integration weight factor.
The final solution gives the integration weight scale factor for the score level fusion [34].
Optimal integration weight estimation using least squares technique was reported in [19].
Reliability-based optimal integration weight estimation for audio-visual decision fusion was reported in [20].
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