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We present a method to estimate recognition performance for large galleries of individuals using data from a significantly smaller gallery. This is achieved by mathematically modelling a cumulative match characteristic (CMC) curve. The similarity scores of the smaller gallery are used to estimate the parameters of the model. After the parameters are estimated, the rank 1 point of the modelled CMC curve is used as our measure of recognition performance. The rank 1 point (i.e.; nearest-neighbor) represents the probability of correctly identifying an individual from a gallery of a particular size; however, as gallery size increases, the rank 1 performance decays. Our model, without making any assumptions about the gallery distribution, replicates this effect, and allows us to estimate recognition performance as gallery size increases without needing to physically add more individuals to the gallery. This model is evaluated on face recognition techniques using a set of faces from the FERET database.