For a given identification technique, in this paper we present a method to
estimate recognition performance for large galleries of individuals using a
significantly smaller gallery to make the estimation. 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
this 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.