Predicting Face Recognition Performance in Unconstrained Environments

P. Jonathon Phillips, Amy N. Yates, J. Ross Beveridge, Geof H. Givens

Abstract: 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.