Predicting Face Recognition Performance in Unconstrained Environments

While face recognition algorithms perform under many different unconstrained conditions, predicting this performance is not possible when a new location is introduced. Analyzing the impostor distribution of the videos of the Point-and-Shoot Challenge (PaSC) as well as its relationship to the genuine match distribution, we present a method for predicting the performance of an algorithm using only unlabeled data for a new location.

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