Extended Fisherface for face recognition from a single example image per person

We extend Fisherface for face recognition from one example image per person. Fisherface is one of the most successful face recognition methods. However, Fisherface requires several training images for each face, so it cannot be applied to face recognition applications where only one example image per person is available for training. To tackle this problem, we extended the Fisherface method by proposing a method to derive multiple images of a face from one single image. Fisherface is then trained on these derived images. Experimental results on the Bern face database and our 350 subjects database show that our method makes impressive performance improvement compared with the conventional eigenfaces and template matching techniques.