Enhanced (PC)2A for face recognition with one training image per person
Recently, a method called (PC)2A was proposed to deal with face recognition with one training image per person. As an extension of the standard eigenface technique, (PC)2A combines linearly each original face image with its corresponding first-order projection into a new face and then performs principal component analysis (PCA) on a set of the newly combined (training) images. It was reported that (PC)2A could achieve higher accuracy than the eigenface technique through using 1015% fewer eigenfaces. In this paper, we generalize and further enhance (PC)2A along two directions. In the first direction, we combine the original image with its second-order projections as well as its first-order projection in order to acquire more information from the original face, and then similarly apply PCA to such a set of the combined images. In the second direction, instead of combining them, we still regard the projections of each original image as single derived images to augment training image set, and then perform PCA on all the training images available, including the original ones and the derived ones. Experiments on the well-known FERET database show that the enhanced versions of (PC)2A are about 1.63.5% more accurate and use about 47.564.8% fewer eigenfaces than (PC)2A.
face recognition eigenface principal component analysis extended pca