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Figure 7 Fusion with a separability measure.
This hypothesis is validated quantitatively using the separability measure.
The measure can also be viewed as a class separability measure.
Figure 21 shows a comparison of the separability measure between ART, FD, GFD, and RCF.
We calculate the d-prime separability measure from the matching score matrix of both the modalities.
This paper provides a new separability measure, the so-called miss-classification probability, in order to overcome such a difficulty.
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Inter-/intra-class separability measures are derived from the feature space of the two modalities.
We have combined the reliability and the separability measures using the mean (average) rule.
Thus, the class separability and the score reliability (dispersion) or separability measures give an indication of the quality of the biometric samples and the matcher [22].
Thus, the class separability and the score separability measures give an indication of the quality of the biometric samples and the matcher.
We propose to reformulate the problem of band selection as a combinatorial problem by modeling an objective function based on class separability measures and the accuracy rate.
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