Face recognition with one training image per person

摘要

At present there are many methods that could deal well with frontal view face recognition. However, most of them cannot work well when there is only one training image per person. In this paper, an extension of the eigenface technique, i.e. projection-combined principal component analysis, (PC)~2A, is proposed. (PC)~2A combines the original face image with its horizontal and vertical projections and then performs principal component analysis on the enriched version of the image. It requires less computational cost than the standard eigenface technique and experimental results shown that on a gray-level frontal view face database where each person has only one training image, (PC)~2A achieves 3-5/100 higher accuracy than the standard eigenface technique through using 10-15/100 fewer eigenfaces.