Comparison of Confidence Measures for Face Recognition
Stefan Eickeler, Mirco Jabs, Gerhard Rigoll
Abstract
This paper compares different confidence measures for the results of
statistical face recognition systems. The main applications of a confidence
measure are rejection of unknown people and the detection of recognition
errors. Some of the confidence measures are based on the posterior
probability and some on the ranking of the recognition results. The
posterior probability is calculated by the Bayes' rule with different ways
to approximate the unconditional likelihood. The confidence measure based on
the ranking is a new method, that is presented in this paper. Experiments to
evaluate the confidence measures are carried out on a pseudo 2-D Hidden
Markov Model based face recognition system and the Bochum face database.