Improving spectroface using pre-processing and voting

In this paper we present pre-processing steps and a voting scheme that improve the effectiveness of the spectroface approach. It consists on a series of pre-processing steps prior to spectroface together with a texture feature that are used independently. The classifier output for each of the 13 features is fused using a majority voting scheme coupled with rules for ties and strong features. Yale (15 subjects), Olivetti (40 subjects) and Notre Dame (487 subjects) face databases are selected to evaluate the proposed method yielding 97.33, 85.28 and 75.91% accuracy, respectively, using only one training image per subject.