Abstract

Face recognition has made significant advances over the last twenty years. State-of-the-art algorithms push the performance envelope to near perfect recognition rates on many face databases. Recently, the Good, the Bad, and the Ugly (GBU) face challenge problem has been introduced to focus on hard aspects of face recognition from still frontal images.

In this paper, we introduce the CohortLDA baseline algorithm, which is an Linear Discriminant Analysis (LDA) algorithm with color spaces and cohort normalization. CohortLDA greatly outperforms some well known face recognition algorithms on the GBU challenge problem. The GBU protocol includes rules for creating training sets. We investigate the effect on performance of violating the rules for creating training sets. This analysis shows that violating the GBU protocol can substantially over estimate performance on the GBU challenge problem.