Parameterising Images for Recognition and Reconstruction

We describe a method based on Principal Component Analysis for extracting a small number of parameters from the whole of an image. These parameters can then be used for characterisation, recognition and reconstruction. The method itself is by no means new, and has a number of obvious flaws. In this paper we suggest improvements, based on purely theoretical considerations, in which the image is preprocessed using prior knowledge of the content. The subsequent Principal Component Analysis (PCA) is both theoretically more attractive, and more effective in practice. We present the work in the context of face recognition, but the method has much wider applicability. One test of the utility of components extracted by PCA is to see how well they represent data not available to the initial analysis. Figure 1 shows our method representing, with only 50 bytes, a face not present in the original ensemble of faces.