a novel face recognition approach based on two-step test sample representation

Abstract

The two-step test sample representation method is proposed for face recognition. It first identifies k “representative” samples from each category training samples for the test sample then produces a weighted sum of all the “representative” samples that well approximates the test sample. This method assigns the test sample to the class whose training samples have the smallest deviation from the test sample. As the method proposed in this paper is able to reduce the side-effect of the other training samples that are very “far” from the test sample on the recognition decision of the test sample, the high recognition rates can be obtained.