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55n = 50 subjects, out of which we randomly pickv ′ , and we perform error-prone pruning accordingto the confusion matrices presented in the Appendix.In Figure 4.6 we demonstrate this pruning gain in an experiment involving the SBS labeled as‘4e’ (see Table 4.1), which employsρ = 4 categories, and which acts on the population that sharesthe distribution of the FERET database.0.950.90.850.8pruning Gain r(vt)0.750.70.650.60.550.50.450 10 20 30 40 50 60 70 80 90 100Iteration tFigure 4.6: SBS ‘4e’, n=50, target subject v ′ belongs to a random category C ′ .For further understanding we proceed with some variations of this simulation.10.980.960.94pruning Gain r(vt)0.920.90.880.860.840.820.80 5 10 15 20 25 30Realization tFigure 4.7: SBS ‘mg’, n=50, target subject v ′ belongs to category C ′ =“moustache - glasses".One point we observed is that generally, even in the presence of the confusion matrices, havingmore categories generally (but not always) translates to a higher gain.We note that for ρ = 16, n = 20, and a target person v ′ who is blue eyed and has no glassesand no moustache, Figure 4.10 reports pruning gain values up to 0.95. This corresponds to thepruning out of 95% of v, which in this specific case is equivalent to saying that the SBS hasentirely identified v ′ .Furthermore we note that, as expected, an increasing authentication group size n generallyintroduces a smaller variability in the gain; see for example Figure 4.9 withn = 200.The Figures 4.7 and 4.8 allow for a gain comparison, where specifically the first considersthe case where v ′ belongs in the rare category of people having glasses and moustache, and in